R-squared (R2) score : The R-squared (R2) score is a metric used in regression analysis to assess how well a model's predictions align with the observed data. Ranging from 0 to 1, a higher R2 indicates a better fit, with 1 representing a perfect fit and 0 indicating no improvement over a mean-based model. The score quantifies the proportion of variance in the dependent variable explained by the independent variables. It is valuable for comparing models and assessing their predictive power. However, limitations exist, and complementary metrics should be considered for a comprehensive evaluation of regression models.

Mean of Test Set:

• Definition: The average (arithmetic mean) of the predicted values or target variable in the test set.
• Use: Provides a central measure of the model's predicted values on the test data.

Standard Deviation of Test Set:

• Definition: A measure of the amount of variation or dispersion of the predicted values around the mean.
• Use: Indicates how spread out the predictions are, helping to assess the model's consistency.

Relative Standard Deviation (Coefficient of Variation):

• Definition: The ratio of the standard deviation to the mean, expressed as a percentage.
• Use: Provides a normalized measure of variability, allowing comparison of variability relative to the mean. Useful for comparing models with different scales.

Feature importance : Feature importance is a concept in machine learning that quantifies the contribution of each input variable (feature) to the model's predictive performance. It helps identify the most influential features in making accurate predictions. Techniques such as tree-based models (e.g., decision trees, random forests) and algorithms like permutation importance are commonly used to assess feature importance. High importance values suggest that a feature has a significant impact on the model's ability to make accurate predictions, aiding in understanding the key drivers behind the model's decision-making process. Feature importance is valuable for feature selection, model interpretation, and enhancing overall model understanding.