- 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 then can be used to compare the models, enabling us to choose the one that gives the best predictions.
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$g(x_i)$ is the prediction -
$y_i$ is the actual -
$m$ is the number of observations in the dataset (i.e. cars)
The entire code of this project is available in this jupyter notebook.
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