RegressionR2SquareComparison

Compare R-Squared and Adjusted R-Squared values for each model and generate a summary table with the results.

Purpose: The purpose of this function is to compare the R-Squared and Adjusted R-Squared values for different models applied to various datasets.

Test Mechanism: The function iterates through each dataset-model pair, calculates the R-Squared and Adjusted R-Squared values, and generates a summary table with these results.

Signs of High Risk: - If the R-Squared values are significantly low, it could indicate that the model is not explaining much of the variability in the dataset. - A significant difference between R-Squared and Adjusted R-Squared values might indicate that the model includes irrelevant features.

Strengths: - Provides a quantitative measure of model performance in terms of variance explained. - Adjusted R-Squared accounts for the number of predictors, making it a more reliable measure when comparing models with different numbers of features.

Limitations: - Assumes that the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns attributes. - The function relies on adj_r2_score from the statsmodels.statsutils module, which should be correctly implemented and imported. - Requires that dataset.y_pred(model) returns the predicted values for the model.