RegressionR2SquareComparison

Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess model performance and relevance of features.

Purpose

The Regression R2 Square Comparison test aims to compare the R-Squared and Adjusted R-Squared values for different regression models across various datasets. It helps in assessing how well each model explains the variability in the dataset, and whether the models include irrelevant features.

Test Mechanism

This test operates by:

• Iterating through each dataset-model pair.
• Calculating the R-Squared values to measure how much of the variability in the dataset is explained by the model.
• Calculating the Adjusted R-Squared values, which adjust the R-Squared based on the number of predictors in the model, making it more reliable when comparing models with different numbers of features.
• Generating a summary table containing these values for each combination of dataset and model.

Signs of High Risk

• If the R-Squared values are significantly low, it indicates the model isn’t 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.
• Useful for time-series forecasting and regression tasks.

Limitations

• Assumes the dataset is provided as a DataFrameDataset object with y, y_pred, and feature_columns attributes.
• Relies on adj_r2_score from the statsmodels.statsutils module, which needs to be correctly implemented and imported.
• Requires that dataset.y_pred(model) returns the predicted values for the model.