Feature Importance — What the Model Uses In Minutes

What's in the report?

Standard-library analysis: of the columns you think explain an outcome, which ones does a model actually depend on? Each candidate predictor is shuffled in turn and the model is re-scored on rows it never trained on, so the fall in performance measures how much the fit really leans on that column. The whole procedure — split, refit, shuffle — is repeated on resampled data, so every importance arrives with an uncertainty interval instead of a single number, and the report says which steps in the ranking are genuinely separated and which are a coin flip. A drop-column importance, the direction of each effect, a collinearity diagnostic that names correlated groups, and the distinction between redundant and irrelevant come with it. Works on any dataset: map a numeric or yes/no outcome and two or more candidate predictors.