Plot types
| Plot | Shows | Read it as |
|---|---|---|
| Bar (mean |SHAP|) | global importance | how much the output moves because of each feature, on average |
| Beeswarm | every row's SHAP, coloured by feature value | direction (red right = high values raise), spread, outliers |
| Dependence / scatter | SHAP vs feature value, coloured by a second feature | shape of the effect, thresholds, interaction (vertical spread by colour) |
| Waterfall | one row, base to prediction | why this prediction |
| Force | the same as waterfall, horizontally | compact local view |
| Decision plot | cumulative paths for many rows | how groups of rows diverge |
| Heatmap | rows x features | clusters of explanations |
e = shap.Explanation(values=sv, base_values=base, data=X.values, feature_names=list(X.columns))
shap.plots.beeswarm(e, max_display=15)
shap.plots.scatter(e[:, "tenure_months"], color=e) # colour by the strongest interacting feature
shap.plots.waterfall(e[17])What shap_summary.py's columns mean
| Column | Meaning | Watch for |
|---|---|---|
| mean|SHAP| | average absolute contribution | compare within one model and one output space only |
| share | fraction of the total | a single feature > 50%: check for leakage |
| mean | average signed contribution | far from 0: the background differs from the explained rows |
| rho | Spearman(value, SHAP) | sign against domain knowledge: proxy, leakage or a data bug |
| spread | SHAP variance not explained by the feature's own value | > 20%: the effect depends on context |
| partner | feature with the strongest within-chunk correlation with the SHAP values | confirm with interaction values or 2-D PDP |
Correlated features
SHAP divides credit between correlated features, and how it divides depends on the explainer:
- Interventional SHAP breaks correlations: it evaluates combinations that may not occur in reality.
- Path-dependent SHAP follows the tree's splits: a feature used first takes more credit.
Mitigations: group correlated features (sum their SHAP) and report the group; drop redundant features and refit; report the correlation next to the ranking.
Pitfalls
| Pitfall | Correct practice |
|---|---|
| "SHAP says raising tenure lowers churn" | SHAP describes the model, not interventions; causal claims need experiments or causal models |
| Ranking with one-hot levels separately | sum levels per input feature first |
| Comparing SHAP across models in different spaces | log-odds and probability values are not comparable |
| Explaining the training set | explain held-out, production-like rows |
| Reading tiny features' shapes | below ~5% share, shapes are mostly noise |
| Trusting a surprising direction | check the data: leakage, encoding errors, target defined after the feature |
| Different backgrounds across reports | fix and document the background |
Wording for stakeholders
- "On average the model predicts 17% churn. For this customer it predicts 94%. The biggest reasons are 12 support calls in the last quarter (strongly raises the risk) and the basic plan (raises it a little)."
- "Across all customers, tenure has the largest influence. Newer customers are scored as riskier."
- Add: "This explains how the model scores customers. It does not prove that changing these factors would change behaviour."
- Avoid raw log-odds in stakeholder text. Use probabilities for the base and the prediction, and words (strongly / slightly raises) for the contributions.
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.