Model Evaluation Measures: Explained Variance Score, MAE, and MSE

Regression models evaluation metrics

Once a model has been built on the training dataset, it is evaluated on the test dataset to measure how well it performs. It will also help us know:

• If the model is overfitting.
• If the model is underfitting.
• If we need to revise our feature engineering or feature selection techniques.

We use the following measures to assess the performance of a regression model.

Explained variance score

Explained variance is one of the key measures in evaluating the regression models. In statistics, explained variation measures the proportion to which a regression model accounts for the variation (dispersion) of a given data set.

Formula

If $\hat{y}$ is the predicted target real-valued output, then $y$ is the corresponding (correct) target real-valued output, and $Var$ is the variance. Then the explained variance is estimated as follows:

$explained\_variance(\hat{y}, y) = 1 - \frac{Var(y-\hat{y})}{Var(y)}$ ...