09-rmse.md

2.9 Root Mean Squared Error (RMSE)

Slides

Notes

• In the previous lesson we found out our predictions were a bit off from the actual target values in the training dataset. We need a way to quantify how good or bad the model is. This is where RMSE can be of help.
• Root Mean Squared Error (RMSE) is a way to evaluate regression models. It measures the error associated with the model being evaluated. This numerical figure can then be used to compare models, enabling us to choose the one that gives the best predictions.

$$RMSE = \sqrt{ \frac{1}{m} \sum_{i=1}^{m} {(g(x_i) - y_i)^2}}$$

• $g(x_i)$ is the prediction
• $y_i$ is the actual value
• $m$ is the number of observations in the dataset (i.e. cars)

The entire code of this project is available in this jupyter notebook.

⚠️	The notes are written by the community. If you see an error here, please create a PR with a fix.

• Notes from Peter Ernicke

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