Definitions
For a feature S and the other features C:
- ICE for row i:
f(x_S = v, x_C = x_C^(i))as v varies over the grid: one curve per row. - PDP: the average of the ICE curves,
PD(v) = mean_i f(v, x_C^(i)). This is the marginal expectation: it pairs every grid value with every row's other features, including combinations that never occur. - Centred ICE (c-ICE):
ICE_i(v) - ICE_i(v_0), so curves start at zero and their shapes are comparable. - ALE: accumulate the average local change in prediction across small intervals of x_S, using only the rows whose x_S falls in each interval:
for each quantile bin k = [z_{k-1}, z_k]:
rows_k = rows with x_S in bin k
effect_k = mean over rows_k of f(z_k, x_C) - f(z_{k-1}, x_C)
ALE(z_k) = sum_{j<=k} effect_j ; then subtract the data-weighted mean so the ALE is centredEach row is only moved within its own neighbourhood, so ALE stays on realistic data when features are correlated. For an additive model ALE recovers each component exactly (the generated script recovers a linear coefficient to 1e-15).
scikit-learn
from sklearn.inspection import partial_dependence, PartialDependenceDisplay
X = X.astype({c: float for c in X.select_dtypes("integer").columns}) # required since 1.6
r = partial_dependence(model, X, ["tenure_months"], kind="both", grid_resolution=20,
percentiles=(0.05, 0.95), response_method="predict_proba")
r["average"], r["individual"], r["grid_values"]
PartialDependenceDisplay.from_estimator(model, X, ["tenure_months", ("tenure_months", "support_calls")],
kind=["both", "average"], centered=True, subsample=300, random_state=0)kind="both"gives ICE + PDP.centered=Truecentres them.subsamplelimits the number of ICE lines.categorical_features=[...]gives a bar-style PDP per level.- For a two-way PDP, the function takes
features=[a, b](a joint grid);from_estimatortakes a tuple(a, b). method="recursion"is a fast exact PDP for sklearn gradient boosting and trees (average only, not ICE).- Multiclass:
r["average"]has one row per class.
Two-way PDP and the H-statistic
A two-way PDP over (a, b) shows the joint effect. Friedman's H-statistic measures how much of it is not additive:
H^2_ab = sum (PD_ab(a,b) - PD_a(a) - PD_b(b))^2 / sum PD_ab(a,b)^2 (all centred)H near 0 means no interaction and H near 1 means the effect is mostly interaction. It is expensive (a grid squared x rows). Screen candidate pairs with ICE separators or SHAP interaction values first.
ALE libraries
alibi.explainers.ALE, PyALE, dalex (model_profile(type="accumulated")), and R's ALEPlot/iml. The
generated script implements first-order ALE for numeric features without extra dependencies.
Cost
| Method | Model evaluations |
|---|---|
| PDP / ICE | rows x grid points (per feature) |
| Two-way PDP | rows x grid^2 |
| ALE | 2 x rows (each row evaluated at its bin's two edges) |
| H-statistic | rows x grid^2 per pair |
Sample 300-1000 rows for ICE. More rows add lines, not insight.
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.