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feature-importance-reporter

Produce honest feature importance reports by computing several methods on held-out data and explaining where they disagree: impurity/gain importance (LightGBM, XGBoost, CatBoost, sklearn MDI), standardised coefficients, permutation importance with repeats and standard deviations, grouped permutation for clusters of correlated features, drop-column importance and mean |SHAP|, all mapped back from encoded columns to input features. Flags features that look important only to impurity measures (split bias, redundancy), features with no measurable held-out effect, identifier-like columns, single features that dominate (leakage), and groups that share information. Use when someone asks which features matter most, needs an importance chart for stakeholders, compares importance methods, or wonders why gain, permutation and SHAP rankings differ.

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20263 references2 scripts

"Which features matter?" has several right answers, and each importance method answers a different question. Gain measures how much the model used a feature while fitting the training data. Permutation importance measures how much held-out performance drops when the feature's information is destroyed. Mean |SHAP| measures how much the feature moves predictions. They disagree for known reasons: split bias, redundancy and leakage. Those disagreements are often the most useful part of the report. scripts/gen_importance_script.py computes the methods on held-out data, and scripts/compare_importances.py lines them up and explains the differences.

Trigger Conditions

Activate this skill when the user:

  • Asks which features matter most, or wants a feature importance chart for a report
  • Sees different rankings from gain, permutation, SHAP or coefficients
  • Wants to drop features and asks which are safe to remove
  • Finds a surprising feature at the top (an ID, a timestamp, a post-outcome field)

Per-prediction explanations and directions: shap-value-explainer. Stability of rankings across folds and seeds: feature-selection-analyzer (importance_stability.py). Shapes of effects: partial-dependence-plot-builder.

Workflow

text
1. HOLD OUT  -> importance is computed on rows the model did not train on (test split or out-of-fold)
2. GENERATE  -> python scripts/gen_importance_script.py --model model.joblib --data holdout.csv --target y --shap -o imp.py
3. COMPUTE   -> python imp.py                      (importances.csv + groups.csv)
4. COMPARE   -> python scripts/compare_importances.py importances.csv --groups groups.csv
5. RESOLVE   -> investigate each flag: leakage, IDs, correlated groups, split bias
6. REPORT    -> one headline method, the others as checks, groups and caveats

What Each Method Measures

MethodQuestion it answersDataBiases
Gain / split (impurity, MDI)how much did the model use it while fitting?trainingfavours continuous and high-cardinality features; inflates noise features the model overfits
|Standardised coefficient|how strongly does the linear predictor depend on it?trainingmeaningless without scaling; unstable under collinearity
Permutation (held-out)how much does performance drop without its information?held-outcorrelated features hide each other; can be negative (noise)
Grouped permutationhow much does a correlated group matter together?held-outgroups must be chosen (correlation threshold)
Drop-columnhow much worse is a model retrained without it?held-out, refitsexpensive; also hides correlated features
Mean |SHAP|how much does it move predictions?anymeasures use, not usefulness; correlated features share credit

Headline recommendation: held-out permutation importance with its spread across repeats, grouped for correlated features. Use SHAP for direction and per-row detail, and impurity importance only as a quick first look. Formulas, settings and code: importance-methods.md.

Reading Disagreements

FlagPatternUsual causeAction
IMPURITY-ONLYhigh gain, little permutationsplit bias or redundancytrust permutation; check correlated partner
SHAP-VS-PERMhigh SHAP, little permutationa correlated feature carries the same informationgroup the pair
NO-EFFECTpermutation within 2 std of 0no generalisable signalcandidate to drop; refit and compare
ID-LIKEidentifier/hash/timestamp namememorisation or leakageremove from features
DOMINANT> 50% of held-out importanceleakage, or a genuinely decisive featurerun target-leakage-detector
SHARED-INFOjoint permutation > 1.3 x the sum of single onesoverlapping informationreport the group

Worked examples and how to tell redundancy from split bias: interpreting-disagreements.md.

Reporting Rules

  • Compute importance on held-out data, with the metric the project optimises.
  • Show uncertainty: permutation std over repeats, or ranges across folds. Do not rank features whose intervals overlap as if the order were meaningful.
  • Group one-hot levels into their input feature, and correlated features into named groups.
  • Say what the numbers mean in units ("shuffling tenure lowers AUC by 0.08"), not only as percentages.
  • Importance is not causation, and a low importance does not prove a feature is useless. The model may get the same information from a correlated feature.
  • Name the model, the data, the date and the method in every chart title.

Chart templates and wording: reporting-importance.md.

Output Format

When reporting feature importance, deliver:

  1. Setup - model, held-out data (rows, period), metric, methods, repeats, grouping threshold.
  2. Headline ranking - permutation importance with std (grouped), in metric units.
  3. Cross-check table - every method's share and rank, with method agreement.
  4. Flags resolved - each flag with the investigation result (dropped, grouped, kept with reason).
  5. Caveats - correlated groups, non-causal reading, data coverage.

Scripts

bash
python scripts/gen_importance_script.py --model model.joblib --data holdout.csv --target churned -o importance.py
python scripts/gen_importance_script.py --model pipe.joblib --data test.csv --target price --scoring neg_root_mean_squared_error
python scripts/gen_importance_script.py --model m.joblib --data test.csv --target y --shap --repeats 20 --corr 0.8

The generated script loads a fitted model or Pipeline and held-out data, reorders the columns to the training order, and computes model-based importance (gain, or coef_x_std for linear models), permutation importance (mean and std over --repeats), grouped permutation for clusters of correlated numeric features (|Spearman| >= --corr) and, with --shap, mean |SHAP|. Encoded columns are summed back to input columns. Verified for LightGBM, XGBoost, random forest and logistic-regression Pipelines and for CatBoost. Exit codes: 0 written, 2 usage error.

bash
python scripts/compare_importances.py importances.csv
python scripts/compare_importances.py importances.csv --groups groups.csv --json

Shares, ranks, consensus rank, method agreement (Spearman) and the flags above. Recognises method kinds from the column names and uses <method>_std columns. Exit codes: 0 compared, 1 a DOMINANT or ID-LIKE flag, 2 usage or read error.

Additional Resources

FileContent
importance-methods.mddefinitions, settings and code for each method, per library
interpreting-disagreements.mdsplit bias, redundancy, leakage, negative importances, worked examples
reporting-importance.mdcharts, uncertainty, grouping, wording, checklist
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