Model evaluation# This section covers techniques and methods to evaluate model performance. Model evaluation Model metrics R-squared and Adjusted R-squared The F-test RMSE Q-squared sklearn metrics for regression problems References Performance metrics Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) Mean Absolute Error (MAE) vs Root Mean Squared Error (RMSE) R-squared (R2) References Train and cross validation References y-Randomization References Partial dependance plots Implementarion in scikit-learn References SHAP (SHapley Additive exPlanations) How SHAP works Applications of SHAP Implementation References
