Skip to content
v0.1.3latest release121 skills4 categories6 sub-categories247 tested scripts397 reference docsContact us
Vector Agentic Skills
Submit a skill

partial-dependence-plot-builder

Build and read partial dependence plots (PDP), individual conditional expectation (ICE) curves and accumulated local effects (ALE) for tabular models: generate scikit-learn partial_dependence scripts with percentile grids, the right response method and class, integer-safe columns, centred ICE with the PDP and a rug of observed values, two-way PDP heatmaps, and ALE that stays correct when features are correlated; then analyse the exported curves for direction, monotonicity, threshold-like jumps, heterogeneity, opposite effects or subgroups of different strength hidden by the average, the feature that separates them (interaction discovery), and grid regions without data support. Use when someone asks how a feature affects predictions, wants PDP/ICE/ALE plots, suspects an interaction, or needs to check that a model's response is monotonic or plausible.

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

A partial dependence plot shows how a model's prediction changes with one feature, averaged over the data. The average hides two things. ICE curves show per-row responses, which reveals whether everyone reacts the same way. ALE removes the unrealistic rows a PDP invents when features are correlated. scripts/gen_pdp_script.py writes a script that draws centred ICE + PDP (+ ALE, + two-way PDP) and exports the curves. scripts/ice_analysis.py reads the curves and reports what the average hides.

Trigger Conditions

Activate this skill when the user:

  • Asks how a feature influences predictions ("what happens to risk as tenure grows?")
  • Wants PDP, ICE, ALE or two-way interaction plots
  • Suspects an interaction or a subgroup that reacts differently
  • Must check that a response is monotonic, plausible or within regulatory expectations

Per-row attributions: shap-value-explainer (SHAP dependence plots are the attribution counterpart). Global rankings: feature-importance-reporter. Smallest change to flip a decision: counterfactual-explanation-generator.

Workflow

text
1. PICK      -> features to show: top of the importance ranking, plus any with a business question
2. GENERATE  -> python scripts/gen_pdp_script.py --model model.joblib --data test.csv --target y --features tenure plan --ale -o pdp.py
3. RUN       -> python pdp.py   (ice_<f>.csv, rows_<f>.csv, pdp_<f>.png [, ale_<f>.csv, pdp2_a_b.png])
4. ANALYSE   -> python scripts/ice_analysis.py ice_tenure.csv --features rows_tenure.csv
5. FOLLOW UP -> interaction found -> two-way PDP (--pair) or ICE coloured by the separator; correlated -> ALE
6. REPORT    -> plot, shape in words, heterogeneity, support, caveats

PDP, ICE or ALE?

SituationUseWhy
First look at a featurecentred ICE + PDPthe average and its spread in one plot
Feature correlated with others (|r| > 0.5)ALEPDP evaluates impossible rows (a 20-year tenure with 1 month of charges)
Suspected interactionICE coloured by the second feature, two-way PDPthe 1-D average cancels opposite effects
Categorical featurePDP / ICE per levelALE needs an ordering
Monotonicity checkICE: share of curves going the wrong waya monotonic PDP can hide non-monotonic rows
Stakeholder chartPDP with a confidence band or ALE, rug of observed valuessimple, honest about support

Centred ICE (each curve minus its value at the first grid point) is the default. Uncentred ICE curves stack by level and hide shape differences. Definitions, maths and the cost of each method: pdp-ice-ale.md.

What ice_analysis.py Reports

FindingRuleMeaning
direction and monotonic sharePDP end minus start; share of steps in that directionoverall shape
steepest segmentlargest change per unit of the gridthreshold-like effect
OPPOSITE EFFECTS>= 10% of curves move against the PDP beyond noisethe average hides an interaction
SUBGROUPSexact two-group split of curve changes, >= 3 pooled sd aparttwo strengths of one effect
separatorfeature with AUC >= 0.7 (or <= 0.3) for the group labelthe likely interaction partner
low supportgrid outside the 5th-95th percentile of observed valuesextrapolation: do not interpret
heterogeneityend-point spread / PDP range > 1the PDP is a poor summary

Verified: the PDP equals sklearn.inspection.partial_dependence's average. On a planted a x sign(b) interaction, the separator is b with AUC 1.00. On the fixture, the separator recovers the planted contract subgroup. ALE recovers the exact coefficient of a linear model with correlated features.

Reading Rules

  • Causal language is wrong. "Predicted churn falls with tenure in this model", not "tenure reduces churn".
  • Interpret only where data exists. Show the rug and ignore the tails outside the observed range.
  • Correlated features make PDP lie. Check correlations first and use ALE when |r| > 0.5.
  • Flat PDP does not mean no effect. Opposite ICE curves can cancel. Check ice_analysis.py.
  • Scale matters. Classifiers: probability (or log-odds for additivity). Say which.
  • Grid percentiles: 5th-95th by default. Extending the grid shows extrapolation, not knowledge.

Reading guide and worked examples: reading-pdp-ice.md. Monotonic constraints, regulatory checks and stakeholder charts: using-pdp-in-practice.md.

Output Format

When presenting partial dependence, deliver:

  1. Setup - model, data and sample size, grid, response (probability / prediction), class.
  2. Plots - centred ICE + PDP per feature with rug; ALE where features correlate; two-way PDP for interactions.
  3. Shape in words - direction, thresholds, monotonicity, magnitude in output units.
  4. Heterogeneity - opposite effects or subgroups, with the separating feature.
  5. Caveats - support, correlation, non-causal reading.

Scripts

bash
python scripts/gen_pdp_script.py --model model.joblib --data test.csv --target churned --features tenure_months support_calls -o pdp.py
python scripts/gen_pdp_script.py --model pipe.joblib --data test.csv --features sqft --ale --sample 500
python scripts/gen_pdp_script.py --model m.joblib --data x.csv --features age --pair age income --class 1

Reorders columns to the training order and converts integer columns to float (scikit-learn refuses integer columns for partial dependence). Uses predict_proba of the last class (or --class) for classifiers, a 5th-95th percentile grid, and level grids for categorical features. Writes the curves, the sampled rows and the PNGs. Verified with LightGBM and logistic-regression Pipelines, numeric and categorical features, ALE and two-way PDP. Exit codes: 0 written, 2 usage error.

bash
python scripts/ice_analysis.py ice_tenure.csv
python scripts/ice_analysis.py ice.csv --features rows.csv --id-col row_id --json

Reads ICE curves in long format (row_id, grid value, prediction); numeric or categorical grids. Exit codes: 0 analysed, 2 usage or read error.

Additional Resources

FileContent
pdp-ice-ale.mddefinitions, centred ICE, ALE algorithm, two-way PDP, H-statistic, cost, library calls
reading-pdp-ice.mdshapes and what they mean, interactions, support, misreadings, worked fixture example
using-pdp-in-practice.mdmonotonic constraints, plausibility reviews, regulated models, stakeholder charts
More in Classical Machine Learning

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.