Python Machine Learning By Example

Regression performance evaluation

So far, we have covered several popular regression algorithms in-depth and implemented them from scratch by using existing 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 that give us more insight:

MSE as we mentioned, measures the squared loss corresponding to the expected value. Sometimes the square root is taken on top of MSE in order to convert the value back to the original scale of the target variable being estimated. This yields the root mean squared error (RMSE).

Mean absolute error (MAE) on the other hand measures the absolute loss. It uses the same scale as the target variable and gives ...