diff --git a/README.MD b/README.MD
index 37b84f51..f596015b 100644
--- a/README.MD
+++ b/README.MD
@@ -19,10 +19,10 @@ See the full documentation [here](noaa-owp.github.io/gval/).
 
 WARNING:
 
-- Our current public API and output formats are likely to change in
-    the future.
-- Software is provided "AS-IS" without any guarantees. Please QA/QC
-    your metrics carefully until this project matures.
+- Our current public API and output formats are likely to change in the
+  future.
+- Software is provided "AS-IS" without any guarantees. Please QA/QC your
+  metrics carefully until this project matures.
 
 # Installation
 
diff --git a/docs/compile_readme_and_arrange_docs.py b/docs/compile_readme_and_arrange_docs.py
index 332572ad..12e56da5 100755
--- a/docs/compile_readme_and_arrange_docs.py
+++ b/docs/compile_readme_and_arrange_docs.py
@@ -101,6 +101,11 @@ def compile_readme():
         f"{abs_path}/sphinx/SphinxContinuousTutorial.ipynb",
     )
 
+    shutil.copy(
+        f"{abs_path}/../notebooks/Multi-Class Categorical Statistics.ipynb",
+        f"{abs_path}/sphinx/SphinxMulticatTutorial.ipynb",
+    )
+
     shutil.copy(
         f"{abs_path}/../CONTRIBUTING.MD",
         f"{abs_path}/sphinx/SPHINX_CONTRIBUTING.MD",
diff --git a/docs/sphinx/PYPI_README.MD b/docs/sphinx/PYPI_README.MD
index ec458e5c..076c4a36 100644
--- a/docs/sphinx/PYPI_README.MD
+++ b/docs/sphinx/PYPI_README.MD
@@ -19,10 +19,10 @@ See the full documentation [here](noaa-owp.github.io/gval/).
 
 WARNING:
 
-- Our current public API and output formats are likely to change in
-    the future.
-- Software is provided "AS-IS" without any guarantees. Please QA/QC
-    your metrics carefully until this project matures.
+- Our current public API and output formats are likely to change in the
+  future.
+- Software is provided "AS-IS" without any guarantees. Please QA/QC your
+  metrics carefully until this project matures.
 
 # Installation
 
diff --git a/docs/sphinx/SPHINX_README.MD b/docs/sphinx/SPHINX_README.MD
index 999fbb8e..77d48eb8 100644
--- a/docs/sphinx/SPHINX_README.MD
+++ b/docs/sphinx/SPHINX_README.MD
@@ -17,10 +17,10 @@ continuous, and probabilistic.
 
 WARNING:
 
-- Our current public API and output formats are likely to change in
-    the future.
-- Software is provided "AS-IS" without any guarantees. Please QA/QC
-    your metrics carefully until this project matures.
+- Our current public API and output formats are likely to change in the
+  future.
+- Software is provided "AS-IS" without any guarantees. Please QA/QC your
+  metrics carefully until this project matures.
 
 # Installation
 
diff --git a/docs/sphinx/SphinxContinuousTutorial.ipynb b/docs/sphinx/SphinxContinuousTutorial.ipynb
index 6d6873a6..6a31f722 100644
--- a/docs/sphinx/SphinxContinuousTutorial.ipynb
+++ b/docs/sphinx/SphinxContinuousTutorial.ipynb
@@ -55,8 +55,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate = rxr.open_rasterio('./livneh_2011_precip.tif', mask_and_scale=True) # VIC\n",
-    "benchmark = rxr.open_rasterio('./prism_2011_precip.tif', mask_and_scale=True) # PRISM"
+    "candidate = rxr.open_rasterio(\n",
+    "    './livneh_2011_precip.tif', mask_and_scale=True\n",
+    ") # VIC\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    './prism_2011_precip.tif', mask_and_scale=True\n",
+    ") # PRISM"
    ]
   },
   {
@@ -110,7 +114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab691960>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9d7ecb0>"
       ]
      },
      "execution_count": 4,
@@ -149,7 +153,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab5a8280>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9c047f0>"
       ]
      },
      "execution_count": 5,
@@ -168,10 +172,12 @@
     }
    ],
    "source": [
-    "agreement.data = xr.where((agreement < np.nanquantile(agreement.values, \n",
-    "                                                      0.0001)) | \n",
-    "                          (agreement > np.nanquantile(agreement.values, 0.9999)), \n",
-    "                          np.nan, agreement)\n",
+    "agreement.data = xr.where(\n",
+    "    (agreement < np.nanquantile(agreement.values, 0.0001)) | \n",
+    "    (agreement > np.nanquantile(agreement.values, 0.9999)), \n",
+    "    np.nan, \n",
+    "    agreement\n",
+    ")\n",
     "agreement.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
   },
@@ -218,36 +224,60 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>band</th>\n",
-       "      <th>coefficient_of_determination</th>\n",
-       "      <th>mean_absolute_error</th>\n",
-       "      <th>mean_absolute_percentage_error</th>\n",
-       "      <th>mean_normalized_mean_absolute_error</th>\n",
-       "      <th>mean_normalized_root_mean_squared_error</th>\n",
-       "      <th>mean_percentage_error</th>\n",
-       "      <th>mean_signed_error</th>\n",
-       "      <th>mean_squared_error</th>\n",
-       "      <th>range_normalized_mean_absolute_error</th>\n",
-       "      <th>range_normalized_root_mean_squared_error</th>\n",
-       "      <th>root_mean_squared_error</th>\n",
-       "      <th>symmetric_mean_absolute_percentage_error</th>\n",
+       "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>band</th>\n",
        "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coefficient_of_determination</th>\n",
        "      <td>0.685261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_absolute_error</th>\n",
        "      <td>216.089706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_absolute_percentage_error</th>\n",
        "      <td>0.319234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_normalized_mean_absolute_error</th>\n",
        "      <td>0.267845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_normalized_root_mean_squared_error</th>\n",
        "      <td>0.372578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_percentage_error</th>\n",
        "      <td>0.010022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_signed_error</th>\n",
        "      <td>8.085411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_squared_error</th>\n",
        "      <td>90351.664062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>range_normalized_mean_absolute_error</th>\n",
        "      <td>0.033065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>range_normalized_root_mean_squared_error</th>\n",
        "      <td>0.045995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>root_mean_squared_error</th>\n",
        "      <td>300.585541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>symmetric_mean_absolute_percentage_error</th>\n",
        "      <td>0.269394</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -255,26 +285,20 @@
        "</div>"
       ],
       "text/plain": [
-       "  band  coefficient_of_determination  mean_absolute_error  \\\n",
-       "0    1                      0.685261           216.089706   \n",
-       "\n",
-       "   mean_absolute_percentage_error  mean_normalized_mean_absolute_error  \\\n",
-       "0                        0.319234                             0.267845   \n",
-       "\n",
-       "   mean_normalized_root_mean_squared_error  mean_percentage_error  \\\n",
-       "0                                 0.372578               0.010022   \n",
-       "\n",
-       "   mean_signed_error  mean_squared_error  \\\n",
-       "0           8.085411        90351.664062   \n",
-       "\n",
-       "   range_normalized_mean_absolute_error  \\\n",
-       "0                              0.033065   \n",
-       "\n",
-       "   range_normalized_root_mean_squared_error  root_mean_squared_error  \\\n",
-       "0                                  0.045995               300.585541   \n",
-       "\n",
-       "   symmetric_mean_absolute_percentage_error  \n",
-       "0                                  0.269394  "
+       "                                                     0\n",
+       "band                                                 1\n",
+       "coefficient_of_determination                  0.685261\n",
+       "mean_absolute_error                         216.089706\n",
+       "mean_absolute_percentage_error                0.319234\n",
+       "mean_normalized_mean_absolute_error           0.267845\n",
+       "mean_normalized_root_mean_squared_error       0.372578\n",
+       "mean_percentage_error                         0.010022\n",
+       "mean_signed_error                             8.085411\n",
+       "mean_squared_error                        90351.664062\n",
+       "range_normalized_mean_absolute_error          0.033065\n",
+       "range_normalized_root_mean_squared_error      0.045995\n",
+       "root_mean_squared_error                     300.585541\n",
+       "symmetric_mean_absolute_percentage_error      0.269394"
       ]
      },
      "execution_count": 6,
@@ -283,7 +307,7 @@
     }
    ],
    "source": [
-    "metric_table"
+    "metric_table.transpose()"
    ]
   },
   {
@@ -325,8 +349,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = \"candidate\")"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = \"candidate\"\n",
+    ")"
    ]
   },
   {
@@ -362,7 +388,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab462530>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c950cb20>"
       ]
      },
      "execution_count": 8,
@@ -381,8 +407,10 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"difference\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"difference\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
@@ -404,7 +432,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab38de70>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9419db0>"
       ]
      },
      "execution_count": 9,
@@ -430,8 +458,10 @@
     "def multiply(c: Number, b: Number) -> Number:\n",
     "    return c / b\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"divide\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"divide\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
@@ -506,9 +536,10 @@
     }
    ],
    "source": [
-    "_, metric_table = candidate.gval.continuous_compare(benchmark,\n",
-    "                                                    metrics=['mean_absolute_error', \n",
-    "                                                             'mean_squared_error'])\n",
+    "_, metric_table = candidate.gval.continuous_compare(\n",
+    "    benchmark,\n",
+    "    metrics=['mean_absolute_error', 'mean_squared_error']\n",
+    ")\n",
     "\n",
     "metric_table"
    ]
@@ -621,10 +652,10 @@
     }
    ],
    "source": [
-    "_, metric_table = candidate.gval.continuous_compare(benchmark,\n",
-    "                                                    metrics=['min_error', \n",
-    "                                                             'median_error', \n",
-    "                                                             'max_error'])\n",
+    "_, metric_table = candidate.gval.continuous_compare(\n",
+    "    benchmark,\n",
+    "    metrics=['min_error', 'median_error', 'max_error']\n",
+    ")\n",
     "\n",
     "metric_table"
    ]
diff --git a/docs/sphinx/SphinxMulticatTutorial.ipynb b/docs/sphinx/SphinxMulticatTutorial.ipynb
new file mode 100644
index 00000000..1f035af2
--- /dev/null
+++ b/docs/sphinx/SphinxMulticatTutorial.ipynb
@@ -0,0 +1,1174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "05d93248",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://github.com/NOAA-OWP/gval/raw/main/docs/images/gval_light_mode.png\" style=\"float:left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4744f004",
+   "metadata": {},
+   "source": [
+    "# Multi-Class Categorical Comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "275a7087",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import rioxarray as rxr\n",
+    "import gval\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "from itertools import product\n",
+    "\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34069943",
+   "metadata": {},
+   "source": [
+    "## Load Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "38473c06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate = rxr.open_rasterio(\n",
+    "    \"./candidate_map_multi_categorical.tif\", mask_and_scale=True\n",
+    ")\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    \"./benchmark_map_multi_categorical.tif\", mask_and_scale=True\n",
+    ")\n",
+    "depth_raster = rxr.open_rasterio(\n",
+    "    \"./candidate_raw_elevation_multi_categorical.tif\", mask_and_scale=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa522035",
+   "metadata": {},
+   "source": [
+    "## Homogenize Datasets and Make Agreement Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e5ca15",
+   "metadata": {},
+   "source": [
+    "Although one can call `candidate.gval.categorical_compare` to run the entire workflow, in this case homogenization and creation of an agreement map will be done separately to show more options for multi-class comparisons."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ac66a26",
+   "metadata": {},
+   "source": [
+    "#### Homogenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "29375e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_r, benchmark_r = candidate.gval.homogenize(benchmark)\n",
+    "depth_raster_r, arb = depth_raster.gval.homogenize(benchmark_r)\n",
+    "del arb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e9e1be1",
+   "metadata": {},
+   "source": [
+    "#### Agreement Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2851c9b",
+   "metadata": {},
+   "source": [
+    "The following makes a pairing dictionary which maps combinations of values in the candidate and benchmark maps to unique values in the agreement map. In this case we will encode each value as concatenation of what the values are.  Instead of making a pairing dictionary one can use the `szudzik` or `cantor` pairing functions to make unique values for each combination of candidate and benchmark map values. e.g. <b>12</b> represents a class <b>1</b> for the candidate and a class <b>2</b> for the benchmark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "de894568",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 1): 11\n",
+      "(1, 2): 12\n",
+      "(1, 3): 13\n",
+      "(1, 4): 14\n",
+      "(1, 5): 15\n",
+      "(2, 1): 21\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = [1, 2, 3, 4, 5]\n",
+    "pairing_dictionary = {(x, y): int(f'{x}{y}') for x, y in product(*([classes]*2))}\n",
+    "\n",
+    "# Showing the first 6 entries\n",
+    "print('\\n'.join([f'{k}: {v}' for k,v  in pairing_dictionary.items()][:6]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44328dcf",
+   "metadata": {},
+   "source": [
+    "The benchmark map has an extra class 0 which is very similar to nodata so it will not be included in `allow_benchmark_values` in the following methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1dc16dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agreement_map = candidate_r.gval.compute_agreement_map(\n",
+    "    benchmark_r,\n",
+    "    nodata=255,\n",
+    "    encode_nodata=True,\n",
+    "    comparison_function=\"pairing_dict\",\n",
+    "    pairing_dict=pairing_dictionary,\n",
+    "    allow_candidate_values=classes,\n",
+    "    allow_benchmark_values=classes,\n",
+    ")\n",
+    "\n",
+    "crosstab = candidate_r.gval.compute_crosstab(\n",
+    "    benchmark_r,\n",
+    "    comparison_function=\"pairing_dict\",\n",
+    "    pairing_dict=pairing_dictionary,\n",
+    "    allow_candidate_values=classes,\n",
+    "    allow_benchmark_values=classes,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93fe86df",
+   "metadata": {},
+   "source": [
+    "The following only shows a small subset of the map for memory purposes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "55606165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fa49e3b4160>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "agreement_map.gval.cat_plot(\n",
+    "    title='Agreement Map', \n",
+    "    figsize=(8, 6),\n",
+    "    colormap='tab20b'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdcbfb8e",
+   "metadata": {},
+   "source": [
+    "## Comparisons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a1f3ecc",
+   "metadata": {},
+   "source": [
+    "For multi-categorical statistics GVAL offers 4 methods of averaging:\n",
+    "\n",
+    "1. No Averaging which provides one vs. all metrics on a class basis\n",
+    "1. Micro Averaging which sums up the contingencies of each class defined as either positive or negative\n",
+    "3. Macro Averaging which sums up the contingencies of one class vs all and then averages them\n",
+    "4. Weighted Averaging which does macro averaging with the inclusion of weights to be applied to each positive category."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66235a0a",
+   "metadata": {},
+   "source": [
+    "### No Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f258087",
+   "metadata": {},
+   "source": [
+    "Using `None` for the averaging argument runs a one class vs. all methodology for each class and reports their metrics on a class basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "936f2dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_categories</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1043.0</td>\n",
+       "      <td>318274.0</td>\n",
+       "      <td>516572.0</td>\n",
+       "      <td>364147.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
+       "      <td>172762.0</td>\n",
+       "      <td>561004.0</td>\n",
+       "      <td>462496.0</td>\n",
+       "      <td>3775.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
+       "      <td>1043592.0</td>\n",
+       "      <td>653360.0</td>\n",
+       "      <td>422623.0</td>\n",
+       "      <td>693617.0</td>\n",
+       "      <td>852206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>953.0</td>\n",
+       "      <td>12967.0</td>\n",
+       "      <td>2396.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.857963</td>\n",
+       "      <td>0.537927</td>\n",
+       "      <td>0.358109</td>\n",
+       "      <td>0.57221</td>\n",
+       "      <td>0.700622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.428984</td>\n",
+       "      <td>0.507741</td>\n",
+       "      <td>0.258311</td>\n",
+       "      <td>0.499602</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001693</td>\n",
+       "      <td>0.016337</td>\n",
+       "      <td>0.004584</td>\n",
+       "      <td>0.000005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.000005</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>-0.175401</td>\n",
+       "      <td>-0.000455</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00338</td>\n",
+       "      <td>0.032148</td>\n",
+       "      <td>0.009125</td>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.998304</td>\n",
+       "      <td>0.972728</td>\n",
+       "      <td>0.611732</td>\n",
+       "      <td>0.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.522545</td>\n",
+       "      <td>0.960853</td>\n",
+       "      <td>0.995383</td>\n",
+       "      <td>0.999995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.001594</td>\n",
+       "      <td>0.429579</td>\n",
+       "      <td>0.426852</td>\n",
+       "      <td>0.299376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.142033</td>\n",
+       "      <td>0.461974</td>\n",
+       "      <td>0.522524</td>\n",
+       "      <td>0.005413</td>\n",
+       "      <td>0.000006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.028455</td>\n",
+       "      <td>0.032675</td>\n",
+       "      <td>0.042339</td>\n",
+       "      <td>0.001253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.000904</td>\n",
+       "      <td>0.001257</td>\n",
+       "      <td>-0.440983</td>\n",
+       "      <td>-0.005543</td>\n",
+       "      <td>-0.000072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.165546</td>\n",
+       "      <td>0.971226</td>\n",
+       "      <td>2.01236</td>\n",
+       "      <td>1.000801</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.999994</td>\n",
+       "      <td>0.998406</td>\n",
+       "      <td>0.570421</td>\n",
+       "      <td>0.573148</td>\n",
+       "      <td>0.700624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>28793.666667</td>\n",
+       "      <td>281.541583</td>\n",
+       "      <td>1.435399</td>\n",
+       "      <td>0.011891</td>\n",
+       "      <td>0.000019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.033511</td>\n",
+       "      <td>0.074919</td>\n",
+       "      <td>0.852916</td>\n",
+       "      <td>0.936112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001696</td>\n",
+       "      <td>0.027272</td>\n",
+       "      <td>0.388268</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.001641</td>\n",
+       "      <td>0.272322</td>\n",
+       "      <td>0.426657</td>\n",
+       "      <td>0.299376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.49588</td>\n",
+       "      <td>0.785107</td>\n",
+       "      <td>0.519876</td>\n",
+       "      <td>0.508252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.857967</td>\n",
+       "      <td>0.538026</td>\n",
+       "      <td>0.477476</td>\n",
+       "      <td>0.994587</td>\n",
+       "      <td>0.999994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.477455</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>0.004617</td>\n",
+       "      <td>0.000005</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             0           1         2  \\\n",
+       "band                                         1           1         1   \n",
+       "positive_categories                          1           2         3   \n",
+       "fn                                         6.0      1043.0  318274.0   \n",
+       "fp                                    172762.0    561004.0  462496.0   \n",
+       "tn                                   1043592.0    653360.0  422623.0   \n",
+       "tp                                         0.0       953.0   12967.0   \n",
+       "accuracy                              0.857963    0.537927  0.358109   \n",
+       "balanced_accuracy                     0.428984    0.507741  0.258311   \n",
+       "critical_success_index                     0.0    0.001693  0.016337   \n",
+       "equitable_threat_score               -0.000005    0.000055 -0.175401   \n",
+       "f_score                                    0.0     0.00338  0.032148   \n",
+       "false_discovery_rate                       1.0    0.998304  0.972728   \n",
+       "false_negative_rate                        1.0    0.522545  0.960853   \n",
+       "false_omission_rate                   0.000006    0.001594  0.429579   \n",
+       "false_positive_rate                   0.142033    0.461974  0.522524   \n",
+       "fowlkes_mallows_index                      0.0    0.028455  0.032675   \n",
+       "matthews_correlation_coefficient     -0.000904    0.001257 -0.440983   \n",
+       "negative_likelihood_ratio             1.165546    0.971226   2.01236   \n",
+       "negative_predictive_value             0.999994    0.998406  0.570421   \n",
+       "overall_bias                      28793.666667  281.541583  1.435399   \n",
+       "positive_likelihood_ratio                  0.0    1.033511  0.074919   \n",
+       "positive_predictive_value                  0.0    0.001696  0.027272   \n",
+       "prevalence                            0.000005    0.001641  0.272322   \n",
+       "prevalence_threshold                       1.0     0.49588  0.785107   \n",
+       "true_negative_rate                    0.857967    0.538026  0.477476   \n",
+       "true_positive_rate                         0.0    0.477455  0.039147   \n",
+       "\n",
+       "                                         3         4  \n",
+       "band                                     1         1  \n",
+       "positive_categories                      4         5  \n",
+       "fn                                516572.0  364147.0  \n",
+       "fp                                  3775.0       5.0  \n",
+       "tn                                693617.0  852206.0  \n",
+       "tp                                  2396.0       2.0  \n",
+       "accuracy                           0.57221  0.700622  \n",
+       "balanced_accuracy                 0.499602       0.5  \n",
+       "critical_success_index            0.004584  0.000005  \n",
+       "equitable_threat_score           -0.000455      -0.0  \n",
+       "f_score                           0.009125  0.000011  \n",
+       "false_discovery_rate              0.611732  0.714286  \n",
+       "false_negative_rate               0.995383  0.999995  \n",
+       "false_omission_rate               0.426852  0.299376  \n",
+       "false_positive_rate               0.005413  0.000006  \n",
+       "fowlkes_mallows_index             0.042339  0.001253  \n",
+       "matthews_correlation_coefficient -0.005543 -0.000072  \n",
+       "negative_likelihood_ratio         1.000801       1.0  \n",
+       "negative_predictive_value         0.573148  0.700624  \n",
+       "overall_bias                      0.011891  0.000019  \n",
+       "positive_likelihood_ratio         0.852916  0.936112  \n",
+       "positive_predictive_value         0.388268  0.285714  \n",
+       "prevalence                        0.426657  0.299376  \n",
+       "prevalence_threshold              0.519876  0.508252  \n",
+       "true_negative_rate                0.994587  0.999994  \n",
+       "true_positive_rate                0.004617  0.000005  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=[1, 2, 3, 4, 5],\n",
+    "    negative_categories=None,\n",
+    "    average=None\n",
+    ")\n",
+    "no_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d722dc68",
+   "metadata": {},
+   "source": [
+    "### Micro Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bbb83cf",
+   "metadata": {},
+   "source": [
+    "The following is an example of a using micro averaging to combine classes to process two-class categorical statistics.  In this example we will use classes <b>1</b> and <b>2</b> as positive classes and classes <b>3</b>, <b>4</b>, and <b>5</b> as negative classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "538dfc49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
+       "      <td>382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
+       "      <td>733099.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
+       "      <td>481259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
+       "      <td>1620.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.396987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.602749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.002204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>0.00056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.004398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.997795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.190809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.000793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.603693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.04224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>0.017033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>0.481468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.999207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>366.992507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>1.340402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.002205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.001646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.463444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.396307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.809191</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           0\n",
+       "band                                       1\n",
+       "fn                                     382.0\n",
+       "fp                                  733099.0\n",
+       "tn                                  481259.0\n",
+       "tp                                    1620.0\n",
+       "accuracy                            0.396987\n",
+       "balanced_accuracy                   0.602749\n",
+       "critical_success_index              0.002204\n",
+       "equitable_threat_score               0.00056\n",
+       "f_score                             0.004398\n",
+       "false_discovery_rate                0.997795\n",
+       "false_negative_rate                 0.190809\n",
+       "false_omission_rate                 0.000793\n",
+       "false_positive_rate                 0.603693\n",
+       "fowlkes_mallows_index                0.04224\n",
+       "matthews_correlation_coefficient    0.017033\n",
+       "negative_likelihood_ratio           0.481468\n",
+       "negative_predictive_value           0.999207\n",
+       "overall_bias                      366.992507\n",
+       "positive_likelihood_ratio           1.340402\n",
+       "positive_predictive_value           0.002205\n",
+       "prevalence                          0.001646\n",
+       "prevalence_threshold                0.463444\n",
+       "true_negative_rate                  0.396307\n",
+       "true_positive_rate                  0.809191"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micro_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=[1, 2],\n",
+    "    negative_categories=[3, 4, 5],\n",
+    "    average=\"micro\"\n",
+    ")\n",
+    "micro_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79761a73",
+   "metadata": {},
+   "source": [
+    "### Macro Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790c56df",
+   "metadata": {},
+   "source": [
+    "The following shows macro averaging and is equivalent to the values of shared columns in `no_averaged_comps.mean()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7e64eb9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.605366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.438927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.004524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.035161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.008933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.85941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.895755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.231481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.22639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.020944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.089249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.229986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.768519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>5815.331112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.579492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.14059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.661823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.77361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.104245</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            0\n",
+       "band                                        1\n",
+       "accuracy                             0.605366\n",
+       "balanced_accuracy                    0.438927\n",
+       "critical_success_index               0.004524\n",
+       "equitable_threat_score              -0.035161\n",
+       "f_score                              0.008933\n",
+       "false_discovery_rate                  0.85941\n",
+       "false_negative_rate                  0.895755\n",
+       "false_omission_rate                  0.231481\n",
+       "false_positive_rate                   0.22639\n",
+       "fowlkes_mallows_index                0.020944\n",
+       "matthews_correlation_coefficient    -0.089249\n",
+       "negative_likelihood_ratio            1.229986\n",
+       "negative_predictive_value            0.768519\n",
+       "overall_bias                      5815.331112\n",
+       "positive_likelihood_ratio            0.579492\n",
+       "positive_predictive_value             0.14059\n",
+       "prevalence                                0.2\n",
+       "prevalence_threshold                 0.661823\n",
+       "true_negative_rate                    0.77361\n",
+       "true_positive_rate                   0.104245"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "macro_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=classes,\n",
+    "    negative_categories=None,\n",
+    "    average=\"macro\"\n",
+    ")\n",
+    "macro_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef8f72ab",
+   "metadata": {},
+   "source": [
+    "### Weighted Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e182a6f7",
+   "metadata": {},
+   "source": [
+    "To further enhance `macro-averaging`, we can apply weights to the classes of interest in order to appropriately change the strength of evaluations for each class.  For instance, if we applied the following vector the classes uses in this notebook, `[1, 4, 1, 5, 1]`, classes <b>2</b> and <b>4</b> would have greater influence on the final averaging of the scores for all classes.  (All weight values are in reference to the other weight values respectively.  e.g. the vector `[5, 5, 5, 5, 5]` would cause no change in the averaging because each weight value is equivalent to a ll other weight values.)  Let's use the first weight vector mentioned in weighted averaging:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0eae1cbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.577454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.476356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.003836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.014789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.007609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.811574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.835662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.239133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.211627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.029953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.03872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.088901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.760867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>2493.443989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.784138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.188426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.225962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.573022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.788373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.164338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            0\n",
+       "band                                        1\n",
+       "accuracy                             0.577454\n",
+       "balanced_accuracy                    0.476356\n",
+       "critical_success_index               0.003836\n",
+       "equitable_threat_score              -0.014789\n",
+       "f_score                              0.007609\n",
+       "false_discovery_rate                 0.811574\n",
+       "false_negative_rate                  0.835662\n",
+       "false_omission_rate                  0.239133\n",
+       "false_positive_rate                  0.211627\n",
+       "fowlkes_mallows_index                0.029953\n",
+       "matthews_correlation_coefficient     -0.03872\n",
+       "negative_likelihood_ratio            1.088901\n",
+       "negative_predictive_value            0.760867\n",
+       "overall_bias                      2493.443989\n",
+       "positive_likelihood_ratio            0.784138\n",
+       "positive_predictive_value            0.188426\n",
+       "prevalence                           0.225962\n",
+       "prevalence_threshold                 0.573022\n",
+       "true_negative_rate                   0.788373\n",
+       "true_positive_rate                   0.164338"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weight_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=classes,\n",
+    "    weights=[1, 4, 1, 5, 1],\n",
+    "    negative_categories=None,\n",
+    "    average=\"weighted\"\n",
+    ")\n",
+    "weight_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c567b77",
+   "metadata": {},
+   "source": [
+    "Regardless of the averaging methodology it seems as though the candidate does not agree with the benchmark.  We can now save the output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d5f7be8",
+   "metadata": {},
+   "source": [
+    "## Save Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dff8f8a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# output agreement map\n",
+    "agreement_file = 'multi_categorical_agreement_map.tif'\n",
+    "metric_file = 'macro_averaged_metric_file.csv'\n",
+    "\n",
+    "agreement_map.rio.to_raster(agreement_file)\n",
+    "macro_averaged_metrics.to_csv(metric_file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/sphinx/SphinxTutorial.ipynb b/docs/sphinx/SphinxTutorial.ipynb
index 8cb24a51..9a9d5b53 100644
--- a/docs/sphinx/SphinxTutorial.ipynb
+++ b/docs/sphinx/SphinxTutorial.ipynb
@@ -13,12 +13,12 @@
    "id": "a403ee30",
    "metadata": {},
    "source": [
-    "# Categorical Comparisons"
+    "# Two-Class Categorical Comparisons"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "a9fa8470",
    "metadata": {},
    "outputs": [],
@@ -45,13 +45,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "f91c0b8c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate = rxr.open_rasterio('candidate_map_two_class_categorical.tif', mask_and_scale=True)\n",
-    "benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_and_scale=True)"
+    "candidate = rxr.open_rasterio(\n",
+    "    'candidate_map_two_class_categorical.tif', mask_and_scale=True\n",
+    ")\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    'benchmark_map_two_class_categorical.tif', mask_and_scale=True\n",
+    ")"
    ]
   },
   {
@@ -72,14 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "541857a7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agreement_map, crosstab_table, metric_table = candidate.gval.categorical_compare(benchmark,\n",
-    "                                                                                 positive_categories=[2],\n",
-    "                                                                                 negative_categories=[0, 1])"
+    "agreement_map, crosstab_table, metric_table = candidate.gval.categorical_compare(\n",
+    "    benchmark,\n",
+    "    positive_categories=[2],\n",
+    "    negative_categories=[0, 1]\n",
+    ")"
    ]
   },
   {
@@ -156,17 +162,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "b1ef13a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1439539ae0>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f09aa6b0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -203,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "fdc9df2b",
    "metadata": {},
    "outputs": [
@@ -280,7 +286,7 @@
        "3    1               2.0               2.0              24.0   2473405.0"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -307,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "16cb3626",
    "metadata": {},
    "outputs": [
@@ -332,91 +338,150 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>band</th>\n",
-       "      <th>fn</th>\n",
-       "      <th>fp</th>\n",
-       "      <th>tn</th>\n",
-       "      <th>tp</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>critical_success_index</th>\n",
-       "      <th>f_score</th>\n",
-       "      <th>false_discovery_rate</th>\n",
-       "      <th>false_negative_rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>fowlkes_mallows_index</th>\n",
-       "      <th>matthews_correlation_coefficient</th>\n",
-       "      <th>negative_likelihood_ratio</th>\n",
-       "      <th>negative_predictive_value</th>\n",
-       "      <th>positive_likelihood_ratio</th>\n",
-       "      <th>positive_predictive_value</th>\n",
-       "      <th>prevalence</th>\n",
-       "      <th>prevalence_threshold</th>\n",
-       "      <th>true_negative_rate</th>\n",
-       "      <th>true_positive_rate</th>\n",
+       "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>band</th>\n",
        "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
        "      <td>639227.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
        "      <td>512277.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
        "      <td>10345720.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
        "      <td>2473405.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
        "      <td>0.917577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.873727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
        "      <td>0.682336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>0.610939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
        "      <td>0.811177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
        "      <td>0.171578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
        "      <td>0.205365</td>\n",
-       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.058191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.04718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
        "      <td>0.811352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
        "      <td>0.758757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
        "      <td>0.215534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
        "      <td>0.941809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>0.959215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
        "      <td>16.842723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
        "      <td>0.828422</td>\n",
-       "      <td>0.213711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.222798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
        "      <td>0.195925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
        "      <td>0.95282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
        "      <td>0.794635</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1 rows × 22 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  band        fn        fp          tn         tp  accuracy  \\\n",
-       "0    1  639227.0  512277.0  10345720.0  2473405.0  0.917577   \n",
-       "\n",
-       "   critical_success_index   f_score  false_discovery_rate  \\\n",
-       "0                0.682336  0.811177              0.171578   \n",
-       "\n",
-       "   false_negative_rate  ...  fowlkes_mallows_index  \\\n",
-       "0             0.205365  ...               0.811352   \n",
-       "\n",
-       "   matthews_correlation_coefficient  negative_likelihood_ratio  \\\n",
-       "0                          0.758757                   0.215534   \n",
-       "\n",
-       "   negative_predictive_value  positive_likelihood_ratio  \\\n",
-       "0                   0.941809                  16.842723   \n",
-       "\n",
-       "   positive_predictive_value  prevalence  prevalence_threshold  \\\n",
-       "0                   0.828422    0.213711              0.195925   \n",
-       "\n",
-       "   true_negative_rate  true_positive_rate  \n",
-       "0             0.95282            0.794635  \n",
-       "\n",
-       "[1 rows x 22 columns]"
+       "                                           0\n",
+       "band                                       1\n",
+       "fn                                  639227.0\n",
+       "fp                                  512277.0\n",
+       "tn                                10345720.0\n",
+       "tp                                 2473405.0\n",
+       "accuracy                            0.917577\n",
+       "balanced_accuracy                   0.873727\n",
+       "critical_success_index              0.682336\n",
+       "equitable_threat_score              0.610939\n",
+       "f_score                             0.811177\n",
+       "false_discovery_rate                0.171578\n",
+       "false_negative_rate                 0.205365\n",
+       "false_omission_rate                 0.058191\n",
+       "false_positive_rate                  0.04718\n",
+       "fowlkes_mallows_index               0.811352\n",
+       "matthews_correlation_coefficient    0.758757\n",
+       "negative_likelihood_ratio           0.215534\n",
+       "negative_predictive_value           0.941809\n",
+       "overall_bias                        0.959215\n",
+       "positive_likelihood_ratio          16.842723\n",
+       "positive_predictive_value           0.828422\n",
+       "prevalence                          0.222798\n",
+       "prevalence_threshold                0.195925\n",
+       "true_negative_rate                   0.95282\n",
+       "true_positive_rate                  0.794635"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metric_table"
+    "metric_table.transpose()"
    ]
   },
   {
@@ -457,13 +522,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "7264ffc9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = \"candidate\")"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = \"candidate\"\n",
+    ")"
    ]
   },
   {
@@ -476,14 +543,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e3917e34",
    "metadata": {},
    "outputs": [],
    "source": [
     "target_map = rxr.open_rasterio('target_map_two_class_categorical.tif')\n",
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = target_map)"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = target_map\n",
+    ")"
    ]
   },
   {
@@ -512,17 +581,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "c6e3c35c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1435a2a8f0>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f38b9000>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -538,8 +607,10 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function='cantor')\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function='cantor'\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -558,17 +629,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "a2310a98",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f143590f430>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f37e7580>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -598,9 +669,11 @@
     "    (2, 2): 8\n",
     "}\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark,\n",
-    "                                                     comparison_function='pairing_dict',\n",
-    "                                                     pairing_dict=pairing_dict)\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark,\n",
+    "    comparison_function='pairing_dict',\n",
+    "    pairing_dict=pairing_dict\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\", basemap=None)"
    ]
@@ -615,17 +688,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "f6567376",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1439598a30>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f73bd180>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -641,10 +714,12 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function='pairing_dict',\n",
-    "                                                     allow_candidate_values=[1, 2],\n",
-    "                                                     allow_benchmark_values=[0, 2])\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function='pairing_dict',\n",
+    "    allow_candidate_values=[1, 2],\n",
+    "    allow_benchmark_values=[0, 2]\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -667,17 +742,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "972f07aa",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1432a72290>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f7334220>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -700,8 +775,10 @@
     "def multiply(c: Number, b: Number) -> Number:\n",
     "    return c * b\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"multi\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"multi\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -732,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "18b9c315",
    "metadata": {},
    "outputs": [
@@ -791,17 +868,18 @@
        "1    1               2.0               2.0               4.0  2624301.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "crosstab_table_allow = candidate.gval.compute_crosstab(benchmark,\n",
-    "                                                       allow_benchmark_values=[0, 2],\n",
-    "                                                       allow_candidate_values=[2],\n",
-    "                                                       comparison_function=\"multi\"\n",
-    "                                                      )\n",
+    "crosstab_table_allow = candidate.gval.compute_crosstab(\n",
+    "    benchmark,\n",
+    "    allow_benchmark_values=[0, 2],\n",
+    "    allow_candidate_values=[2],\n",
+    "    comparison_function=\"multi\"\n",
+    ")\n",
     "crosstab_table_allow"
    ]
   },
@@ -823,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "2ba3fc06",
    "metadata": {},
    "outputs": [
@@ -866,7 +944,7 @@
        "      <td>10345720.0</td>\n",
        "      <td>2473405.0</td>\n",
        "      <td>0.794635</td>\n",
-       "      <td>0.213711</td>\n",
+       "      <td>0.222798</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -877,18 +955,20 @@
        "0    1  639227.0  512277.0  10345720.0  2473405.0            0.794635   \n",
        "\n",
        "   prevalence  \n",
-       "0    0.213711  "
+       "0    0.222798  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metric_table_select = crosstab_table.gval.compute_categorical_metrics(negative_categories= [0, 1],\n",
-    "                                                                      positive_categories = [2],\n",
-    "                                                                      metrics=['true_positive_rate', 'prevalence'])\n",
+    "metric_table_select = crosstab_table.gval.compute_categorical_metrics(\n",
+    "    negative_categories= [0, 1],\n",
+    "    positive_categories = [2],\n",
+    "    metrics=['true_positive_rate', 'prevalence']\n",
+    ")\n",
     "metric_table_select"
    ]
   },
@@ -902,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "67938408",
    "metadata": {},
    "outputs": [],
@@ -924,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "1e8eeb59",
    "metadata": {},
    "outputs": [],
@@ -951,21 +1031,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "6a41eee3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_table_register = crosstab_table.gval.compute_categorical_metrics(negative_categories= None,\n",
-    "                                                                        positive_categories = [2],\n",
-    "                                                                        metrics=['error_balance', \n",
-    "                                                                                 'arbitrary1', \n",
-    "                                                                                 'arbitrary2'])"
+    "metric_table_register = crosstab_table.gval.compute_categorical_metrics(\n",
+    "    negative_categories= None,\n",
+    "    positive_categories = [2],\n",
+    "    metrics=['error_balance', 'arbitrary1', 'arbitrary2']\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "6ab884b7",
    "metadata": {},
    "outputs": [
@@ -1017,7 +1097,7 @@
        "0    1  639227.0  512277.0 NaN  2473405.0       0.801401"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "899a1da9",
    "metadata": {},
    "outputs": [],
diff --git a/docs/sphinx/index.rst b/docs/sphinx/index.rst
index 940661d6..01e883ba 100644
--- a/docs/sphinx/index.rst
+++ b/docs/sphinx/index.rst
@@ -17,6 +17,7 @@ ___________________________________
    :caption: Table of Contents
 
    SphinxTutorial
+   SphinxMulticatTutorial
    SphinxContinuousTutorial
    api
    contributing
diff --git a/docs/sphinx/tutorials.rst b/docs/sphinx/tutorials.rst
new file mode 100644
index 00000000..847a6575
--- /dev/null
+++ b/docs/sphinx/tutorials.rst
@@ -0,0 +1,10 @@
+Tutorials
+#########
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Table of Contents
+
+   SphinxTutorial
+   SphinxMulticatTutorial
+   SphinxContinuousTutorial
diff --git a/notebooks/Continuous Comparison Tutorial.ipynb b/notebooks/Continuous Comparison Tutorial.ipynb
index 6d6873a6..6a31f722 100644
--- a/notebooks/Continuous Comparison Tutorial.ipynb	
+++ b/notebooks/Continuous Comparison Tutorial.ipynb	
@@ -55,8 +55,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate = rxr.open_rasterio('./livneh_2011_precip.tif', mask_and_scale=True) # VIC\n",
-    "benchmark = rxr.open_rasterio('./prism_2011_precip.tif', mask_and_scale=True) # PRISM"
+    "candidate = rxr.open_rasterio(\n",
+    "    './livneh_2011_precip.tif', mask_and_scale=True\n",
+    ") # VIC\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    './prism_2011_precip.tif', mask_and_scale=True\n",
+    ") # PRISM"
    ]
   },
   {
@@ -110,7 +114,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab691960>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9d7ecb0>"
       ]
      },
      "execution_count": 4,
@@ -149,7 +153,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab5a8280>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9c047f0>"
       ]
      },
      "execution_count": 5,
@@ -168,10 +172,12 @@
     }
    ],
    "source": [
-    "agreement.data = xr.where((agreement < np.nanquantile(agreement.values, \n",
-    "                                                      0.0001)) | \n",
-    "                          (agreement > np.nanquantile(agreement.values, 0.9999)), \n",
-    "                          np.nan, agreement)\n",
+    "agreement.data = xr.where(\n",
+    "    (agreement < np.nanquantile(agreement.values, 0.0001)) | \n",
+    "    (agreement > np.nanquantile(agreement.values, 0.9999)), \n",
+    "    np.nan, \n",
+    "    agreement\n",
+    ")\n",
     "agreement.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
   },
@@ -218,36 +224,60 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>band</th>\n",
-       "      <th>coefficient_of_determination</th>\n",
-       "      <th>mean_absolute_error</th>\n",
-       "      <th>mean_absolute_percentage_error</th>\n",
-       "      <th>mean_normalized_mean_absolute_error</th>\n",
-       "      <th>mean_normalized_root_mean_squared_error</th>\n",
-       "      <th>mean_percentage_error</th>\n",
-       "      <th>mean_signed_error</th>\n",
-       "      <th>mean_squared_error</th>\n",
-       "      <th>range_normalized_mean_absolute_error</th>\n",
-       "      <th>range_normalized_root_mean_squared_error</th>\n",
-       "      <th>root_mean_squared_error</th>\n",
-       "      <th>symmetric_mean_absolute_percentage_error</th>\n",
+       "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>band</th>\n",
        "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coefficient_of_determination</th>\n",
        "      <td>0.685261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_absolute_error</th>\n",
        "      <td>216.089706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_absolute_percentage_error</th>\n",
        "      <td>0.319234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_normalized_mean_absolute_error</th>\n",
        "      <td>0.267845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_normalized_root_mean_squared_error</th>\n",
        "      <td>0.372578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_percentage_error</th>\n",
        "      <td>0.010022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_signed_error</th>\n",
        "      <td>8.085411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean_squared_error</th>\n",
        "      <td>90351.664062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>range_normalized_mean_absolute_error</th>\n",
        "      <td>0.033065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>range_normalized_root_mean_squared_error</th>\n",
        "      <td>0.045995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>root_mean_squared_error</th>\n",
        "      <td>300.585541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>symmetric_mean_absolute_percentage_error</th>\n",
        "      <td>0.269394</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -255,26 +285,20 @@
        "</div>"
       ],
       "text/plain": [
-       "  band  coefficient_of_determination  mean_absolute_error  \\\n",
-       "0    1                      0.685261           216.089706   \n",
-       "\n",
-       "   mean_absolute_percentage_error  mean_normalized_mean_absolute_error  \\\n",
-       "0                        0.319234                             0.267845   \n",
-       "\n",
-       "   mean_normalized_root_mean_squared_error  mean_percentage_error  \\\n",
-       "0                                 0.372578               0.010022   \n",
-       "\n",
-       "   mean_signed_error  mean_squared_error  \\\n",
-       "0           8.085411        90351.664062   \n",
-       "\n",
-       "   range_normalized_mean_absolute_error  \\\n",
-       "0                              0.033065   \n",
-       "\n",
-       "   range_normalized_root_mean_squared_error  root_mean_squared_error  \\\n",
-       "0                                  0.045995               300.585541   \n",
-       "\n",
-       "   symmetric_mean_absolute_percentage_error  \n",
-       "0                                  0.269394  "
+       "                                                     0\n",
+       "band                                                 1\n",
+       "coefficient_of_determination                  0.685261\n",
+       "mean_absolute_error                         216.089706\n",
+       "mean_absolute_percentage_error                0.319234\n",
+       "mean_normalized_mean_absolute_error           0.267845\n",
+       "mean_normalized_root_mean_squared_error       0.372578\n",
+       "mean_percentage_error                         0.010022\n",
+       "mean_signed_error                             8.085411\n",
+       "mean_squared_error                        90351.664062\n",
+       "range_normalized_mean_absolute_error          0.033065\n",
+       "range_normalized_root_mean_squared_error      0.045995\n",
+       "root_mean_squared_error                     300.585541\n",
+       "symmetric_mean_absolute_percentage_error      0.269394"
       ]
      },
      "execution_count": 6,
@@ -283,7 +307,7 @@
     }
    ],
    "source": [
-    "metric_table"
+    "metric_table.transpose()"
    ]
   },
   {
@@ -325,8 +349,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = \"candidate\")"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = \"candidate\"\n",
+    ")"
    ]
   },
   {
@@ -362,7 +388,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab462530>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c950cb20>"
       ]
      },
      "execution_count": 8,
@@ -381,8 +407,10 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"difference\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"difference\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
@@ -404,7 +432,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fd7ab38de70>"
+       "<matplotlib.collections.QuadMesh at 0x7f45c9419db0>"
       ]
      },
      "execution_count": 9,
@@ -430,8 +458,10 @@
     "def multiply(c: Number, b: Number) -> Number:\n",
     "    return c / b\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"divide\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"divide\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cont_plot(title=\"Agreement Map\", figsize=(6, 3))"
    ]
@@ -506,9 +536,10 @@
     }
    ],
    "source": [
-    "_, metric_table = candidate.gval.continuous_compare(benchmark,\n",
-    "                                                    metrics=['mean_absolute_error', \n",
-    "                                                             'mean_squared_error'])\n",
+    "_, metric_table = candidate.gval.continuous_compare(\n",
+    "    benchmark,\n",
+    "    metrics=['mean_absolute_error', 'mean_squared_error']\n",
+    ")\n",
     "\n",
     "metric_table"
    ]
@@ -621,10 +652,10 @@
     }
    ],
    "source": [
-    "_, metric_table = candidate.gval.continuous_compare(benchmark,\n",
-    "                                                    metrics=['min_error', \n",
-    "                                                             'median_error', \n",
-    "                                                             'max_error'])\n",
+    "_, metric_table = candidate.gval.continuous_compare(\n",
+    "    benchmark,\n",
+    "    metrics=['min_error', 'median_error', 'max_error']\n",
+    ")\n",
     "\n",
     "metric_table"
    ]
diff --git a/notebooks/Multi-Class Categorical Statistics.ipynb b/notebooks/Multi-Class Categorical Statistics.ipynb
new file mode 100644
index 00000000..1f035af2
--- /dev/null
+++ b/notebooks/Multi-Class Categorical Statistics.ipynb	
@@ -0,0 +1,1174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "05d93248",
+   "metadata": {},
+   "source": [
+    "<img src=\"https://github.com/NOAA-OWP/gval/raw/main/docs/images/gval_light_mode.png\" style=\"float:left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4744f004",
+   "metadata": {},
+   "source": [
+    "# Multi-Class Categorical Comparisons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "275a7087",
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import rioxarray as rxr\n",
+    "import gval\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import xarray as xr\n",
+    "from itertools import product\n",
+    "\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34069943",
+   "metadata": {},
+   "source": [
+    "## Load Datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "38473c06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate = rxr.open_rasterio(\n",
+    "    \"./candidate_map_multi_categorical.tif\", mask_and_scale=True\n",
+    ")\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    \"./benchmark_map_multi_categorical.tif\", mask_and_scale=True\n",
+    ")\n",
+    "depth_raster = rxr.open_rasterio(\n",
+    "    \"./candidate_raw_elevation_multi_categorical.tif\", mask_and_scale=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa522035",
+   "metadata": {},
+   "source": [
+    "## Homogenize Datasets and Make Agreement Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e5ca15",
+   "metadata": {},
+   "source": [
+    "Although one can call `candidate.gval.categorical_compare` to run the entire workflow, in this case homogenization and creation of an agreement map will be done separately to show more options for multi-class comparisons."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ac66a26",
+   "metadata": {},
+   "source": [
+    "#### Homogenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "29375e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "candidate_r, benchmark_r = candidate.gval.homogenize(benchmark)\n",
+    "depth_raster_r, arb = depth_raster.gval.homogenize(benchmark_r)\n",
+    "del arb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e9e1be1",
+   "metadata": {},
+   "source": [
+    "#### Agreement Map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e2851c9b",
+   "metadata": {},
+   "source": [
+    "The following makes a pairing dictionary which maps combinations of values in the candidate and benchmark maps to unique values in the agreement map. In this case we will encode each value as concatenation of what the values are.  Instead of making a pairing dictionary one can use the `szudzik` or `cantor` pairing functions to make unique values for each combination of candidate and benchmark map values. e.g. <b>12</b> represents a class <b>1</b> for the candidate and a class <b>2</b> for the benchmark."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "de894568",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 1): 11\n",
+      "(1, 2): 12\n",
+      "(1, 3): 13\n",
+      "(1, 4): 14\n",
+      "(1, 5): 15\n",
+      "(2, 1): 21\n"
+     ]
+    }
+   ],
+   "source": [
+    "classes = [1, 2, 3, 4, 5]\n",
+    "pairing_dictionary = {(x, y): int(f'{x}{y}') for x, y in product(*([classes]*2))}\n",
+    "\n",
+    "# Showing the first 6 entries\n",
+    "print('\\n'.join([f'{k}: {v}' for k,v  in pairing_dictionary.items()][:6]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44328dcf",
+   "metadata": {},
+   "source": [
+    "The benchmark map has an extra class 0 which is very similar to nodata so it will not be included in `allow_benchmark_values` in the following methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1dc16dd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agreement_map = candidate_r.gval.compute_agreement_map(\n",
+    "    benchmark_r,\n",
+    "    nodata=255,\n",
+    "    encode_nodata=True,\n",
+    "    comparison_function=\"pairing_dict\",\n",
+    "    pairing_dict=pairing_dictionary,\n",
+    "    allow_candidate_values=classes,\n",
+    "    allow_benchmark_values=classes,\n",
+    ")\n",
+    "\n",
+    "crosstab = candidate_r.gval.compute_crosstab(\n",
+    "    benchmark_r,\n",
+    "    comparison_function=\"pairing_dict\",\n",
+    "    pairing_dict=pairing_dictionary,\n",
+    "    allow_candidate_values=classes,\n",
+    "    allow_benchmark_values=classes,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93fe86df",
+   "metadata": {},
+   "source": [
+    "The following only shows a small subset of the map for memory purposes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "55606165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7fa49e3b4160>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "agreement_map.gval.cat_plot(\n",
+    "    title='Agreement Map', \n",
+    "    figsize=(8, 6),\n",
+    "    colormap='tab20b'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdcbfb8e",
+   "metadata": {},
+   "source": [
+    "## Comparisons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a1f3ecc",
+   "metadata": {},
+   "source": [
+    "For multi-categorical statistics GVAL offers 4 methods of averaging:\n",
+    "\n",
+    "1. No Averaging which provides one vs. all metrics on a class basis\n",
+    "1. Micro Averaging which sums up the contingencies of each class defined as either positive or negative\n",
+    "3. Macro Averaging which sums up the contingencies of one class vs all and then averages them\n",
+    "4. Weighted Averaging which does macro averaging with the inclusion of weights to be applied to each positive category."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66235a0a",
+   "metadata": {},
+   "source": [
+    "### No Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f258087",
+   "metadata": {},
+   "source": [
+    "Using `None` for the averaging argument runs a one class vs. all methodology for each class and reports their metrics on a class basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "936f2dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_categories</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1043.0</td>\n",
+       "      <td>318274.0</td>\n",
+       "      <td>516572.0</td>\n",
+       "      <td>364147.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
+       "      <td>172762.0</td>\n",
+       "      <td>561004.0</td>\n",
+       "      <td>462496.0</td>\n",
+       "      <td>3775.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
+       "      <td>1043592.0</td>\n",
+       "      <td>653360.0</td>\n",
+       "      <td>422623.0</td>\n",
+       "      <td>693617.0</td>\n",
+       "      <td>852206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>953.0</td>\n",
+       "      <td>12967.0</td>\n",
+       "      <td>2396.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.857963</td>\n",
+       "      <td>0.537927</td>\n",
+       "      <td>0.358109</td>\n",
+       "      <td>0.57221</td>\n",
+       "      <td>0.700622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.428984</td>\n",
+       "      <td>0.507741</td>\n",
+       "      <td>0.258311</td>\n",
+       "      <td>0.499602</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001693</td>\n",
+       "      <td>0.016337</td>\n",
+       "      <td>0.004584</td>\n",
+       "      <td>0.000005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.000005</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>-0.175401</td>\n",
+       "      <td>-0.000455</td>\n",
+       "      <td>-0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00338</td>\n",
+       "      <td>0.032148</td>\n",
+       "      <td>0.009125</td>\n",
+       "      <td>0.000011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.998304</td>\n",
+       "      <td>0.972728</td>\n",
+       "      <td>0.611732</td>\n",
+       "      <td>0.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.522545</td>\n",
+       "      <td>0.960853</td>\n",
+       "      <td>0.995383</td>\n",
+       "      <td>0.999995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.000006</td>\n",
+       "      <td>0.001594</td>\n",
+       "      <td>0.429579</td>\n",
+       "      <td>0.426852</td>\n",
+       "      <td>0.299376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.142033</td>\n",
+       "      <td>0.461974</td>\n",
+       "      <td>0.522524</td>\n",
+       "      <td>0.005413</td>\n",
+       "      <td>0.000006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.028455</td>\n",
+       "      <td>0.032675</td>\n",
+       "      <td>0.042339</td>\n",
+       "      <td>0.001253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.000904</td>\n",
+       "      <td>0.001257</td>\n",
+       "      <td>-0.440983</td>\n",
+       "      <td>-0.005543</td>\n",
+       "      <td>-0.000072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.165546</td>\n",
+       "      <td>0.971226</td>\n",
+       "      <td>2.01236</td>\n",
+       "      <td>1.000801</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.999994</td>\n",
+       "      <td>0.998406</td>\n",
+       "      <td>0.570421</td>\n",
+       "      <td>0.573148</td>\n",
+       "      <td>0.700624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>28793.666667</td>\n",
+       "      <td>281.541583</td>\n",
+       "      <td>1.435399</td>\n",
+       "      <td>0.011891</td>\n",
+       "      <td>0.000019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.033511</td>\n",
+       "      <td>0.074919</td>\n",
+       "      <td>0.852916</td>\n",
+       "      <td>0.936112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001696</td>\n",
+       "      <td>0.027272</td>\n",
+       "      <td>0.388268</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.000005</td>\n",
+       "      <td>0.001641</td>\n",
+       "      <td>0.272322</td>\n",
+       "      <td>0.426657</td>\n",
+       "      <td>0.299376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.49588</td>\n",
+       "      <td>0.785107</td>\n",
+       "      <td>0.519876</td>\n",
+       "      <td>0.508252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.857967</td>\n",
+       "      <td>0.538026</td>\n",
+       "      <td>0.477476</td>\n",
+       "      <td>0.994587</td>\n",
+       "      <td>0.999994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.477455</td>\n",
+       "      <td>0.039147</td>\n",
+       "      <td>0.004617</td>\n",
+       "      <td>0.000005</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             0           1         2  \\\n",
+       "band                                         1           1         1   \n",
+       "positive_categories                          1           2         3   \n",
+       "fn                                         6.0      1043.0  318274.0   \n",
+       "fp                                    172762.0    561004.0  462496.0   \n",
+       "tn                                   1043592.0    653360.0  422623.0   \n",
+       "tp                                         0.0       953.0   12967.0   \n",
+       "accuracy                              0.857963    0.537927  0.358109   \n",
+       "balanced_accuracy                     0.428984    0.507741  0.258311   \n",
+       "critical_success_index                     0.0    0.001693  0.016337   \n",
+       "equitable_threat_score               -0.000005    0.000055 -0.175401   \n",
+       "f_score                                    0.0     0.00338  0.032148   \n",
+       "false_discovery_rate                       1.0    0.998304  0.972728   \n",
+       "false_negative_rate                        1.0    0.522545  0.960853   \n",
+       "false_omission_rate                   0.000006    0.001594  0.429579   \n",
+       "false_positive_rate                   0.142033    0.461974  0.522524   \n",
+       "fowlkes_mallows_index                      0.0    0.028455  0.032675   \n",
+       "matthews_correlation_coefficient     -0.000904    0.001257 -0.440983   \n",
+       "negative_likelihood_ratio             1.165546    0.971226   2.01236   \n",
+       "negative_predictive_value             0.999994    0.998406  0.570421   \n",
+       "overall_bias                      28793.666667  281.541583  1.435399   \n",
+       "positive_likelihood_ratio                  0.0    1.033511  0.074919   \n",
+       "positive_predictive_value                  0.0    0.001696  0.027272   \n",
+       "prevalence                            0.000005    0.001641  0.272322   \n",
+       "prevalence_threshold                       1.0     0.49588  0.785107   \n",
+       "true_negative_rate                    0.857967    0.538026  0.477476   \n",
+       "true_positive_rate                         0.0    0.477455  0.039147   \n",
+       "\n",
+       "                                         3         4  \n",
+       "band                                     1         1  \n",
+       "positive_categories                      4         5  \n",
+       "fn                                516572.0  364147.0  \n",
+       "fp                                  3775.0       5.0  \n",
+       "tn                                693617.0  852206.0  \n",
+       "tp                                  2396.0       2.0  \n",
+       "accuracy                           0.57221  0.700622  \n",
+       "balanced_accuracy                 0.499602       0.5  \n",
+       "critical_success_index            0.004584  0.000005  \n",
+       "equitable_threat_score           -0.000455      -0.0  \n",
+       "f_score                           0.009125  0.000011  \n",
+       "false_discovery_rate              0.611732  0.714286  \n",
+       "false_negative_rate               0.995383  0.999995  \n",
+       "false_omission_rate               0.426852  0.299376  \n",
+       "false_positive_rate               0.005413  0.000006  \n",
+       "fowlkes_mallows_index             0.042339  0.001253  \n",
+       "matthews_correlation_coefficient -0.005543 -0.000072  \n",
+       "negative_likelihood_ratio         1.000801       1.0  \n",
+       "negative_predictive_value         0.573148  0.700624  \n",
+       "overall_bias                      0.011891  0.000019  \n",
+       "positive_likelihood_ratio         0.852916  0.936112  \n",
+       "positive_predictive_value         0.388268  0.285714  \n",
+       "prevalence                        0.426657  0.299376  \n",
+       "prevalence_threshold              0.519876  0.508252  \n",
+       "true_negative_rate                0.994587  0.999994  \n",
+       "true_positive_rate                0.004617  0.000005  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=[1, 2, 3, 4, 5],\n",
+    "    negative_categories=None,\n",
+    "    average=None\n",
+    ")\n",
+    "no_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d722dc68",
+   "metadata": {},
+   "source": [
+    "### Micro Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bbb83cf",
+   "metadata": {},
+   "source": [
+    "The following is an example of a using micro averaging to combine classes to process two-class categorical statistics.  In this example we will use classes <b>1</b> and <b>2</b> as positive classes and classes <b>3</b>, <b>4</b>, and <b>5</b> as negative classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "538dfc49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
+       "      <td>382.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
+       "      <td>733099.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
+       "      <td>481259.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
+       "      <td>1620.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.396987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.602749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.002204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>0.00056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.004398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.997795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.190809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.000793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.603693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.04224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>0.017033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>0.481468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.999207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>366.992507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>1.340402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.002205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.001646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.463444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.396307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.809191</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           0\n",
+       "band                                       1\n",
+       "fn                                     382.0\n",
+       "fp                                  733099.0\n",
+       "tn                                  481259.0\n",
+       "tp                                    1620.0\n",
+       "accuracy                            0.396987\n",
+       "balanced_accuracy                   0.602749\n",
+       "critical_success_index              0.002204\n",
+       "equitable_threat_score               0.00056\n",
+       "f_score                             0.004398\n",
+       "false_discovery_rate                0.997795\n",
+       "false_negative_rate                 0.190809\n",
+       "false_omission_rate                 0.000793\n",
+       "false_positive_rate                 0.603693\n",
+       "fowlkes_mallows_index                0.04224\n",
+       "matthews_correlation_coefficient    0.017033\n",
+       "negative_likelihood_ratio           0.481468\n",
+       "negative_predictive_value           0.999207\n",
+       "overall_bias                      366.992507\n",
+       "positive_likelihood_ratio           1.340402\n",
+       "positive_predictive_value           0.002205\n",
+       "prevalence                          0.001646\n",
+       "prevalence_threshold                0.463444\n",
+       "true_negative_rate                  0.396307\n",
+       "true_positive_rate                  0.809191"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micro_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=[1, 2],\n",
+    "    negative_categories=[3, 4, 5],\n",
+    "    average=\"micro\"\n",
+    ")\n",
+    "micro_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79761a73",
+   "metadata": {},
+   "source": [
+    "### Macro Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "790c56df",
+   "metadata": {},
+   "source": [
+    "The following shows macro averaging and is equivalent to the values of shared columns in `no_averaged_comps.mean()`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7e64eb9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.605366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.438927</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.004524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.035161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.008933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.85941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.895755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.231481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.22639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.020944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.089249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.229986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.768519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>5815.331112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.579492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.14059</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.661823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.77361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.104245</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            0\n",
+       "band                                        1\n",
+       "accuracy                             0.605366\n",
+       "balanced_accuracy                    0.438927\n",
+       "critical_success_index               0.004524\n",
+       "equitable_threat_score              -0.035161\n",
+       "f_score                              0.008933\n",
+       "false_discovery_rate                  0.85941\n",
+       "false_negative_rate                  0.895755\n",
+       "false_omission_rate                  0.231481\n",
+       "false_positive_rate                   0.22639\n",
+       "fowlkes_mallows_index                0.020944\n",
+       "matthews_correlation_coefficient    -0.089249\n",
+       "negative_likelihood_ratio            1.229986\n",
+       "negative_predictive_value            0.768519\n",
+       "overall_bias                      5815.331112\n",
+       "positive_likelihood_ratio            0.579492\n",
+       "positive_predictive_value             0.14059\n",
+       "prevalence                                0.2\n",
+       "prevalence_threshold                 0.661823\n",
+       "true_negative_rate                    0.77361\n",
+       "true_positive_rate                   0.104245"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "macro_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=classes,\n",
+    "    negative_categories=None,\n",
+    "    average=\"macro\"\n",
+    ")\n",
+    "macro_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef8f72ab",
+   "metadata": {},
+   "source": [
+    "### Weighted Averaging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e182a6f7",
+   "metadata": {},
+   "source": [
+    "To further enhance `macro-averaging`, we can apply weights to the classes of interest in order to appropriately change the strength of evaluations for each class.  For instance, if we applied the following vector the classes uses in this notebook, `[1, 4, 1, 5, 1]`, classes <b>2</b> and <b>4</b> would have greater influence on the final averaging of the scores for all classes.  (All weight values are in reference to the other weight values respectively.  e.g. the vector `[5, 5, 5, 5, 5]` would cause no change in the averaging because each weight value is equivalent to a ll other weight values.)  Let's use the first weight vector mentioned in weighted averaging:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0eae1cbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>band</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
+       "      <td>0.577454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.476356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
+       "      <td>0.003836</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>-0.014789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
+       "      <td>0.007609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
+       "      <td>0.811574</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
+       "      <td>0.835662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.239133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.211627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
+       "      <td>0.029953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
+       "      <td>-0.03872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
+       "      <td>1.088901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
+       "      <td>0.760867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>2493.443989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
+       "      <td>0.784138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
+       "      <td>0.188426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.225962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
+       "      <td>0.573022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
+       "      <td>0.788373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
+       "      <td>0.164338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            0\n",
+       "band                                        1\n",
+       "accuracy                             0.577454\n",
+       "balanced_accuracy                    0.476356\n",
+       "critical_success_index               0.003836\n",
+       "equitable_threat_score              -0.014789\n",
+       "f_score                              0.007609\n",
+       "false_discovery_rate                 0.811574\n",
+       "false_negative_rate                  0.835662\n",
+       "false_omission_rate                  0.239133\n",
+       "false_positive_rate                  0.211627\n",
+       "fowlkes_mallows_index                0.029953\n",
+       "matthews_correlation_coefficient     -0.03872\n",
+       "negative_likelihood_ratio            1.088901\n",
+       "negative_predictive_value            0.760867\n",
+       "overall_bias                      2493.443989\n",
+       "positive_likelihood_ratio            0.784138\n",
+       "positive_predictive_value            0.188426\n",
+       "prevalence                           0.225962\n",
+       "prevalence_threshold                 0.573022\n",
+       "true_negative_rate                   0.788373\n",
+       "true_positive_rate                   0.164338"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weight_averaged_metrics = crosstab.gval.compute_categorical_metrics(\n",
+    "    positive_categories=classes,\n",
+    "    weights=[1, 4, 1, 5, 1],\n",
+    "    negative_categories=None,\n",
+    "    average=\"weighted\"\n",
+    ")\n",
+    "weight_averaged_metrics.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c567b77",
+   "metadata": {},
+   "source": [
+    "Regardless of the averaging methodology it seems as though the candidate does not agree with the benchmark.  We can now save the output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d5f7be8",
+   "metadata": {},
+   "source": [
+    "## Save Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dff8f8a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# output agreement map\n",
+    "agreement_file = 'multi_categorical_agreement_map.tif'\n",
+    "metric_file = 'macro_averaged_metric_file.csv'\n",
+    "\n",
+    "agreement_map.rio.to_raster(agreement_file)\n",
+    "macro_averaged_metrics.to_csv(metric_file)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Tutorial.ipynb b/notebooks/Tutorial.ipynb
index 8cb24a51..9a9d5b53 100644
--- a/notebooks/Tutorial.ipynb
+++ b/notebooks/Tutorial.ipynb
@@ -13,12 +13,12 @@
    "id": "a403ee30",
    "metadata": {},
    "source": [
-    "# Categorical Comparisons"
+    "# Two-Class Categorical Comparisons"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "a9fa8470",
    "metadata": {},
    "outputs": [],
@@ -45,13 +45,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "f91c0b8c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate = rxr.open_rasterio('candidate_map_two_class_categorical.tif', mask_and_scale=True)\n",
-    "benchmark = rxr.open_rasterio('benchmark_map_two_class_categorical.tif', mask_and_scale=True)"
+    "candidate = rxr.open_rasterio(\n",
+    "    'candidate_map_two_class_categorical.tif', mask_and_scale=True\n",
+    ")\n",
+    "benchmark = rxr.open_rasterio(\n",
+    "    'benchmark_map_two_class_categorical.tif', mask_and_scale=True\n",
+    ")"
    ]
   },
   {
@@ -72,14 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "541857a7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "agreement_map, crosstab_table, metric_table = candidate.gval.categorical_compare(benchmark,\n",
-    "                                                                                 positive_categories=[2],\n",
-    "                                                                                 negative_categories=[0, 1])"
+    "agreement_map, crosstab_table, metric_table = candidate.gval.categorical_compare(\n",
+    "    benchmark,\n",
+    "    positive_categories=[2],\n",
+    "    negative_categories=[0, 1]\n",
+    ")"
    ]
   },
   {
@@ -156,17 +162,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "b1ef13a0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1439539ae0>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f09aa6b0>"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -203,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "fdc9df2b",
    "metadata": {},
    "outputs": [
@@ -280,7 +286,7 @@
        "3    1               2.0               2.0              24.0   2473405.0"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -307,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "16cb3626",
    "metadata": {},
    "outputs": [
@@ -332,91 +338,150 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>band</th>\n",
-       "      <th>fn</th>\n",
-       "      <th>fp</th>\n",
-       "      <th>tn</th>\n",
-       "      <th>tp</th>\n",
-       "      <th>accuracy</th>\n",
-       "      <th>critical_success_index</th>\n",
-       "      <th>f_score</th>\n",
-       "      <th>false_discovery_rate</th>\n",
-       "      <th>false_negative_rate</th>\n",
-       "      <th>...</th>\n",
-       "      <th>fowlkes_mallows_index</th>\n",
-       "      <th>matthews_correlation_coefficient</th>\n",
-       "      <th>negative_likelihood_ratio</th>\n",
-       "      <th>negative_predictive_value</th>\n",
-       "      <th>positive_likelihood_ratio</th>\n",
-       "      <th>positive_predictive_value</th>\n",
-       "      <th>prevalence</th>\n",
-       "      <th>prevalence_threshold</th>\n",
-       "      <th>true_negative_rate</th>\n",
-       "      <th>true_positive_rate</th>\n",
+       "      <th>0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>band</th>\n",
        "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fn</th>\n",
        "      <td>639227.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fp</th>\n",
        "      <td>512277.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tn</th>\n",
        "      <td>10345720.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tp</th>\n",
        "      <td>2473405.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>accuracy</th>\n",
        "      <td>0.917577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>balanced_accuracy</th>\n",
+       "      <td>0.873727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>critical_success_index</th>\n",
        "      <td>0.682336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>equitable_threat_score</th>\n",
+       "      <td>0.610939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>f_score</th>\n",
        "      <td>0.811177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_discovery_rate</th>\n",
        "      <td>0.171578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_negative_rate</th>\n",
        "      <td>0.205365</td>\n",
-       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_omission_rate</th>\n",
+       "      <td>0.058191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>false_positive_rate</th>\n",
+       "      <td>0.04718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fowlkes_mallows_index</th>\n",
        "      <td>0.811352</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>matthews_correlation_coefficient</th>\n",
        "      <td>0.758757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_likelihood_ratio</th>\n",
        "      <td>0.215534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>negative_predictive_value</th>\n",
        "      <td>0.941809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>overall_bias</th>\n",
+       "      <td>0.959215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_likelihood_ratio</th>\n",
        "      <td>16.842723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>positive_predictive_value</th>\n",
        "      <td>0.828422</td>\n",
-       "      <td>0.213711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence</th>\n",
+       "      <td>0.222798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prevalence_threshold</th>\n",
        "      <td>0.195925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_negative_rate</th>\n",
        "      <td>0.95282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>true_positive_rate</th>\n",
        "      <td>0.794635</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1 rows × 22 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  band        fn        fp          tn         tp  accuracy  \\\n",
-       "0    1  639227.0  512277.0  10345720.0  2473405.0  0.917577   \n",
-       "\n",
-       "   critical_success_index   f_score  false_discovery_rate  \\\n",
-       "0                0.682336  0.811177              0.171578   \n",
-       "\n",
-       "   false_negative_rate  ...  fowlkes_mallows_index  \\\n",
-       "0             0.205365  ...               0.811352   \n",
-       "\n",
-       "   matthews_correlation_coefficient  negative_likelihood_ratio  \\\n",
-       "0                          0.758757                   0.215534   \n",
-       "\n",
-       "   negative_predictive_value  positive_likelihood_ratio  \\\n",
-       "0                   0.941809                  16.842723   \n",
-       "\n",
-       "   positive_predictive_value  prevalence  prevalence_threshold  \\\n",
-       "0                   0.828422    0.213711              0.195925   \n",
-       "\n",
-       "   true_negative_rate  true_positive_rate  \n",
-       "0             0.95282            0.794635  \n",
-       "\n",
-       "[1 rows x 22 columns]"
+       "                                           0\n",
+       "band                                       1\n",
+       "fn                                  639227.0\n",
+       "fp                                  512277.0\n",
+       "tn                                10345720.0\n",
+       "tp                                 2473405.0\n",
+       "accuracy                            0.917577\n",
+       "balanced_accuracy                   0.873727\n",
+       "critical_success_index              0.682336\n",
+       "equitable_threat_score              0.610939\n",
+       "f_score                             0.811177\n",
+       "false_discovery_rate                0.171578\n",
+       "false_negative_rate                 0.205365\n",
+       "false_omission_rate                 0.058191\n",
+       "false_positive_rate                  0.04718\n",
+       "fowlkes_mallows_index               0.811352\n",
+       "matthews_correlation_coefficient    0.758757\n",
+       "negative_likelihood_ratio           0.215534\n",
+       "negative_predictive_value           0.941809\n",
+       "overall_bias                        0.959215\n",
+       "positive_likelihood_ratio          16.842723\n",
+       "positive_predictive_value           0.828422\n",
+       "prevalence                          0.222798\n",
+       "prevalence_threshold                0.195925\n",
+       "true_negative_rate                   0.95282\n",
+       "true_positive_rate                  0.794635"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metric_table"
+    "metric_table.transpose()"
    ]
   },
   {
@@ -457,13 +522,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "7264ffc9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = \"candidate\")"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = \"candidate\"\n",
+    ")"
    ]
   },
   {
@@ -476,14 +543,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "e3917e34",
    "metadata": {},
    "outputs": [],
    "source": [
     "target_map = rxr.open_rasterio('target_map_two_class_categorical.tif')\n",
-    "candidate, benchmark = candidate.gval.homogenize(benchmark_map=benchmark,\n",
-    "                                                 target_map = target_map)"
+    "candidate, benchmark = candidate.gval.homogenize(\n",
+    "    benchmark_map=benchmark,\n",
+    "    target_map = target_map\n",
+    ")"
    ]
   },
   {
@@ -512,17 +581,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "c6e3c35c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1435a2a8f0>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f38b9000>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -538,8 +607,10 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function='cantor')\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function='cantor'\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -558,17 +629,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "a2310a98",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f143590f430>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f37e7580>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -598,9 +669,11 @@
     "    (2, 2): 8\n",
     "}\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark,\n",
-    "                                                     comparison_function='pairing_dict',\n",
-    "                                                     pairing_dict=pairing_dict)\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark,\n",
+    "    comparison_function='pairing_dict',\n",
+    "    pairing_dict=pairing_dict\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\", basemap=None)"
    ]
@@ -615,17 +688,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "f6567376",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1439598a30>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f73bd180>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -641,10 +714,12 @@
     }
    ],
    "source": [
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function='pairing_dict',\n",
-    "                                                     allow_candidate_values=[1, 2],\n",
-    "                                                     allow_benchmark_values=[0, 2])\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function='pairing_dict',\n",
+    "    allow_candidate_values=[1, 2],\n",
+    "    allow_benchmark_values=[0, 2]\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -667,17 +742,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "972f07aa",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f1432a72290>"
+       "<matplotlib.collections.QuadMesh at 0x7fd0f7334220>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -700,8 +775,10 @@
     "def multiply(c: Number, b: Number) -> Number:\n",
     "    return c * b\n",
     "\n",
-    "agreement_map = candidate.gval.compute_agreement_map(benchmark_map=benchmark, \n",
-    "                                                     comparison_function=\"multi\")\n",
+    "agreement_map = candidate.gval.compute_agreement_map(\n",
+    "    benchmark_map=benchmark, \n",
+    "    comparison_function=\"multi\"\n",
+    ")\n",
     "\n",
     "agreement_map.gval.cat_plot(title=\"Agreement Map\")"
    ]
@@ -732,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "18b9c315",
    "metadata": {},
    "outputs": [
@@ -791,17 +868,18 @@
        "1    1               2.0               2.0               4.0  2624301.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "crosstab_table_allow = candidate.gval.compute_crosstab(benchmark,\n",
-    "                                                       allow_benchmark_values=[0, 2],\n",
-    "                                                       allow_candidate_values=[2],\n",
-    "                                                       comparison_function=\"multi\"\n",
-    "                                                      )\n",
+    "crosstab_table_allow = candidate.gval.compute_crosstab(\n",
+    "    benchmark,\n",
+    "    allow_benchmark_values=[0, 2],\n",
+    "    allow_candidate_values=[2],\n",
+    "    comparison_function=\"multi\"\n",
+    ")\n",
     "crosstab_table_allow"
    ]
   },
@@ -823,7 +901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "2ba3fc06",
    "metadata": {},
    "outputs": [
@@ -866,7 +944,7 @@
        "      <td>10345720.0</td>\n",
        "      <td>2473405.0</td>\n",
        "      <td>0.794635</td>\n",
-       "      <td>0.213711</td>\n",
+       "      <td>0.222798</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -877,18 +955,20 @@
        "0    1  639227.0  512277.0  10345720.0  2473405.0            0.794635   \n",
        "\n",
        "   prevalence  \n",
-       "0    0.213711  "
+       "0    0.222798  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "metric_table_select = crosstab_table.gval.compute_categorical_metrics(negative_categories= [0, 1],\n",
-    "                                                                      positive_categories = [2],\n",
-    "                                                                      metrics=['true_positive_rate', 'prevalence'])\n",
+    "metric_table_select = crosstab_table.gval.compute_categorical_metrics(\n",
+    "    negative_categories= [0, 1],\n",
+    "    positive_categories = [2],\n",
+    "    metrics=['true_positive_rate', 'prevalence']\n",
+    ")\n",
     "metric_table_select"
    ]
   },
@@ -902,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "67938408",
    "metadata": {},
    "outputs": [],
@@ -924,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "1e8eeb59",
    "metadata": {},
    "outputs": [],
@@ -951,21 +1031,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "6a41eee3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "metric_table_register = crosstab_table.gval.compute_categorical_metrics(negative_categories= None,\n",
-    "                                                                        positive_categories = [2],\n",
-    "                                                                        metrics=['error_balance', \n",
-    "                                                                                 'arbitrary1', \n",
-    "                                                                                 'arbitrary2'])"
+    "metric_table_register = crosstab_table.gval.compute_categorical_metrics(\n",
+    "    negative_categories= None,\n",
+    "    positive_categories = [2],\n",
+    "    metrics=['error_balance', 'arbitrary1', 'arbitrary2']\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "6ab884b7",
    "metadata": {},
    "outputs": [
@@ -1017,7 +1097,7 @@
        "0    1  639227.0  512277.0 NaN  2473405.0       0.801401"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "899a1da9",
    "metadata": {},
    "outputs": [],
diff --git a/notebooks/benchmark_map_multi_categorical.tif b/notebooks/benchmark_map_multi_categorical.tif
new file mode 100644
index 00000000..941e1f23
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diff --git a/notebooks/candidate_map_multi_categorical.tif b/notebooks/candidate_map_multi_categorical.tif
new file mode 100644
index 00000000..2e334cf3
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diff --git a/notebooks/candidate_raw_elevation_multi_categorical.tif b/notebooks/candidate_raw_elevation_multi_categorical.tif
new file mode 100644
index 00000000..90ba5d3d
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