So far, we've covered in depth four popular regression algorithms and implemented them from scratch and by using several prominent libraries. Instead of judging how well a model works on testing sets by printing out the prediction, we need to evaluate its performance by the following metrics which give us better insight:
- The MSE, as we mentioned, measures the squared loss corresponding to the expected value. Sometimes the square root is taken on top of the MSE in order to convert the value back into the original scale of the target variable being estimated. This yields the root mean squared error (RMSE).
- The mean absolute error (MAE) on the other hand measures the absolute loss. It uses the same scale as the target variable and gives an idea of how close predictions are to the actual values.
For both the MSE and MAE, the smaller value,...
