diff --git a/fastFM/als.py b/fastFM/als.py
index 513a9dd..8ec1db0 100644
--- a/fastFM/als.py
+++ b/fastFM/als.py
@@ -169,6 +169,10 @@ def fit(self, X_train, y_train):
         y_train = _validate_class_labels(y_train)
 
         self.classes_ = np.unique(y_train)
+        if len(self.classes_) != 2:
+            raise ValueError("This solver only supports binary classification"
+                             " but the data contains"
+                             " class: %r" % self.classes_)
 
         # fastFM-core expects labels to be in {-1,1}
         y_train = y_train.copy()
diff --git a/fastFM/mcmc.py b/fastFM/mcmc.py
index 4ea6bf4..fa841e5 100644
--- a/fastFM/mcmc.py
+++ b/fastFM/mcmc.py
@@ -211,6 +211,10 @@ def fit_predict_proba(self, X_train, y_train, X_test):
         self.task = "classification"
 
         self.classes_ = np.unique(y_train)
+        if len(self.classes_) != 2:
+            raise ValueError("This solver only supports binary classification"
+                             " but the data contains"
+                             " class: %r" % self.classes_)
 
         # fastFM-core expects labels to be in {-1,1}
         y_train = y_train.copy()
@@ -218,10 +222,6 @@ def fit_predict_proba(self, X_train, y_train, X_test):
         y_train[i_class1] = -1
         y_train[-i_class1] = 1
 
-        if len(self.classes_) != 2:
-            raise ValueError("This solver only supports binary classification"
-                             "but the data contains"
-                             " class: %r" % self.classes_[0])
 
         X_train, y_train, X_test = _validate_mcmc_fit_input(X_train, y_train,
                                                                     X_test)
diff --git a/fastFM/sgd.py b/fastFM/sgd.py
index 07df241..34aca45 100644
--- a/fastFM/sgd.py
+++ b/fastFM/sgd.py
@@ -158,6 +158,10 @@ def fit(self, X, y):
         """
         y = _validate_class_labels(y)
         self.classes_ = np.unique(y)
+        if len(self.classes_) != 2:
+            raise ValueError("This solver only supports binary classification"
+                             " but the data contains"
+                             " class: %r" % self.classes_)
 
         # fastFM-core expects labels to be in {-1,1}
         y_train = y.copy()
@@ -165,11 +169,6 @@ def fit(self, X, y):
         y_train[i_class1] = -1
         y_train[-i_class1] = 1
 
-        if len(self.classes_) != 2:
-            raise ValueError("This solver only supports binary classification"
-                             "but the data contains"
-                             " class: %r" % self.classes_[0])
-
         check_consistent_length(X, y)
         y = y.astype(np.float64)
         X = X.T