Split bias (IMPURITY-ONLY on a lone feature)
Tree learners pick the best split among many candidate thresholds. A continuous or high-cardinality feature offers many more candidates, so by chance alone it finds splits that reduce training impurity. MDI and gain then rank it highly even when it carries no signal. Strobl et al. (2007) showed this for random forests.
Recognise it:
- high gain share, permutation near zero or negative
- the feature has many distinct values (IDs, hashes, fine-grained timestamps, raw zip codes)
- no strongly correlated partner exists
Action: trust the held-out measure. For IDs, remove the feature. For legitimate continuous features, check binned versions or monotone constraints.
Redundancy (IMPURITY-ONLY or SHAP-VS-PERM with a correlated partner)
total_charges is roughly tenure_months x monthly_charges. The model splits on both, so gain and SHAP
credit both. Permuting total_charges alone hardly hurts because tenure_months still carries most of the
information.
Recognise it:
- |correlation| >= 0.7 with another feature (
gen_importance_script.pybuilds the groups) - grouped permutation is larger than the sum of single permutations (SHARED-INFO)
Action: report the group's importance. If you must drop one member, drop by domain sense (keep the interpretable one) and confirm with a drop-column refit.
Leakage (DOMINANT)
A single feature with more than half the held-out importance, especially one that is recorded after the outcome (refund issued, cancellation reason, account closed date), points to leakage. Run target-leakage-detector. A genuinely decisive feature (e.g. a credit bureau score) also dominates, so the timing of the data decides.
Negative permutation importance
Shuffling the feature made the score better. Within noise (|mean| < 2 std) it means no effect. Clearly negative means the model learned a pattern that does not hold out of sample, which is a sign of overfitting on that feature. Drop it or regularise.
Methods agree but the order changes between runs
Use importance_stability.py from
feature-selection-analyzer. If the top-k set changes across folds,
the ranking is not a finding. Report tiers (clearly important / marginal / no effect) instead of ranks.
Worked example (fixture importances.csv)
| Feature | gain | permutation | mean|SHAP| | Reading |
|---|---|---|---|---|
| tenure_months | 24% | 39% | 32% | top on every method: a robust driver |
| total_charges | 22% | 2% | 11% | redundant with tenure x monthly charges: group them |
| monthly_charges | 11% | 1% | 4% | split bias / redundancy: little unique signal |
| customer_id_hash | 9% | -0.5% | 2% | ID-like split bias: remove |
| region_north | 0.7% | 0% | 0.4% | no effect |
Importance versus usefulness versus causality
| Statement | Supported by |
|---|---|
| "The model relies on X" | SHAP, gain |
| "Removing X's information hurts held-out performance" | permutation, drop-column |
| "We could drop X without losing accuracy" | drop-column refit (and grouped permutation for correlated sets) |
| "Changing X changes the outcome" | none of these; needs experiments or causal inference |
anomaly-detection-model-builder
Build and evaluate anomaly detection for tabular, sensor and transaction data with few or no labels: define the anomaly type (point, contextual, collective), choose detectors (robust z-score/MAD and IQR rules, Mahalanobis with robust covariance, k-NN distance, LOF, Isolation Forest, one-class SVM, PCA reconstruction / autoencoders, forecasting residuals for time series), combine them by rank, explain each alert by its deviating features, set thresholds from an alert budget or the score distribution, and evaluate with precision@k, PR-AUC and recall on known incidents, or detector agreement when no labels exist. Includes pure-Python detectors (robust z, Mahalanobis, kNN, Isolation Forest, ensemble) and an evaluation script.
catboost-model-builder
Build CatBoost models that use its strengths correctly: raw categorical columns as cat_features (ordered target statistics, no manual encoding), text_features, Pool construction with NaN-safe categories, ordered boosting and has_time for temporal data, symmetric (oblivious) trees, overfitting detector with eval_set and use_best_model, losses for classification, regression (Quantile, Poisson, Tweedie) and ranking (YetiRank), class weights, monotone constraints, feature importance (PredictionValuesChange, LossFunctionChange) and SHAP values, GPU training and model export. Plans cat/text/numeric columns from a CSV and generates a runnable training script, and checks parameter sets for the conflicts CatBoost rejects (iterations vs n_estimators, Bayesian bootstrap with subsample, multiple class-weight options, depth > 16, use_best_model without eval_set).
categorical-encoding-selector
Choose and implement the right encoding for every categorical column given the model family and the data: one-hot (with infrequent-level grouping and handle_unknown), ordinal with an explicit order, cross-fitted target encoding (scikit-learn TargetEncoder, CatBoost ordered statistics, category_encoders inside CV), frequency/count encoding, feature hashing, native categorical support in LightGBM, HistGradientBoosting, XGBoost and CatBoost, prefix levels for codes (zip3, NAICS-2), and embeddings. Measures cardinality, rare-level share, unseen-level rate on a time-ordered holdout and target signal per column, emits the ColumnTransformer, and quantifies target-encoding leakage (in-sample vs out-of-fold vs holdout AUC).
classification-model-selector
Choose the right classification algorithm for tabular, text or mixed data and prove the choice with a fair bake-off: logistic regression (L2, L1, elastic net), linear SVM, naive Bayes, k-NN, RBF SVM, decision trees, random forest, extra trees, HistGradientBoosting, LightGBM, XGBoost, CatBoost, explainable boosting machines (EBM/GAM), points scorecards and MLPs, matched to data size, width, categorical and high-cardinality share, missingness, class imbalance, text, and constraints such as interpretability, monotonic constraints, calibrated probabilities, latency budgets and tiny deployable models. Ranks candidates with starting hyperparameters and cautions, then generates a bake-off script running every candidate on identical CV folds with per-fold metrics, fit time and prediction latency.
clustering-algorithm-selector
Choose the right clustering algorithm and check that clustering makes sense at all: cluster tendency (Hopkins statistic with a correlated-features caveat, nearest-neighbour isolation), then k-means / MiniBatchKMeans, Gaussian mixtures with BIC, agglomerative (Ward, average, complete, single) with dendrograms, DBSCAN, HDBSCAN, OPTICS, spectral clustering, BIRCH, mean shift, k-prototypes and k-modes for mixed or categorical data, and Gower distance, matched to data size, expected shapes, noise, varying density, known k, soft memberships, hierarchy and streaming. Covers preprocessing (scaling, encoding, dimensionality, distance metrics), validation (internal indices, bootstrap stability, external labels, business profiling) and assigning new points.
collaborative-filtering-implementer
Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.