FeatureImportanceComparison

Compare feature importance scores for each model and generate a summary table with the top important features.

Purpose: The purpose of this function is to compare the feature importance scores for different models applied to various datasets.

Test Mechanism: The function iterates through each dataset-model pair, calculates permutation feature importance (PFI) scores, and generates a summary table with the top num_features important features for each model.

Signs of High Risk: - If key features expected to be important are ranked low, it could indicate potential issues with model training or data quality. - High variance in feature importance scores across different models may suggest instability in feature selection.

Strengths: - Provides a clear comparison of the most important features for each model. - Uses permutation importance, which is a model-agnostic method and can be applied to any estimator.

Limitations: - Assumes that the dataset is provided as a DataFrameDataset object with x_df and y_df methods to access feature and target data. - Requires that model.model is compatible with sklearn.inspection.permutation_importance. - The function’s output is dependent on the number of features specified by num_features, which defaults to 3 but can be adjusted.