I used the R-squared metric for cross-validation last week, which estimates the fraction of variation in the dependent variable that is predictable given predictors. Today, I attempted analysing my models using several scoring measures, reading about their differences. Notably, in the absence of a stated scoring metric in the parameters, the cross_val_score function calculates the negative Mean Squared Error (MSE) for each fold, a metric that is extremely sensitive to outliers. Furthermore, I learned about the Mean Absolute Error (MAE) measure, which should be used when all errors should be given equal weightage.