Last week, I employed the R-squared metric for cross-validation, which helps estimate how much of the variation in the dependent variable can be predicted based on the predictors. Today, I delved into analyzing my models using various scoring measures and took some time to understand their distinctions. Notably, when a specific scoring metric isn’t specified in the parameters, the cross_val_score function calculates the negative Mean Squared Error (MSE) for each fold. It’s important to note that MSE is highly sensitive to outliers.

Additionally, I familiarized myself with the Mean Absolute Error (MAE) measure, which is more appropriate when all errors should be treated with equal importance and weight. This metric provides a different perspective on model performance compared to MSE and is particularly useful in scenarios where outliers can significantly impact results.