Choosing slices
- Sensitive attributes relevant to the domain and jurisdiction (e.g. sex, age, ethnicity, disability). Collecting or using them, even for evaluation only, is regulated in some places, so check with legal.
- Proxies when the attributes are unavailable: geography, language, device. Use them with care.
- Business segments: plan, region, channel, tenure band. Performance often differs most here.
- Intersections (group x age band) when sample sizes allow.
- Data-quality strata: new vs established customers, complete vs sparse records.
Metrics and what they mean
| Metric | Definition | Fits when |
|---|---|---|
| Demographic parity difference | positive rate max - min | the benefit or burden should reach groups equally regardless of the label |
| Disparate impact ratio | positive rate min / max (< 0.8: four-fifths rule, US EEOC guideline) | screening, selection |
| Equal opportunity difference | TPR max - min | missing a true positive is the harm (e.g. not offering help to someone who needs it) |
| FPR difference | FPR max - min | false alarms are the harm (e.g. fraud flags, extra scrutiny) |
| Equalised odds | both TPR and FPR equal | both error types matter |
| Calibration within groups | predicted probability = observed rate in each group | scores are used as probabilities |
| AUC per group | ranking quality per group | the model's usefulness differs across groups |
When base rates differ between groups, calibration and equalised odds cannot both hold, except with a perfect model (Kleinberg et al. 2016; Chouldechova 2017). Choose the notion from the decision's harms and document why.
Small groups
- Report n and positives;
slice_metrics.pymarks LOW-N (< 50 rows or < 10 positives or negatives) and leaves those slices out of the gaps. - Use the bootstrap CIs: a gap whose intervals overlap heavily may be noise.
- Pool periods or use Bayesian shrinkage for rare groups instead of over-reading a point estimate.
Mitigation options
| Stage | Option | Trade-off |
|---|---|---|
| Data | collect more data for under-served groups; fix label bias | slow, most durable |
| Features | remove proxies; add features that serve all groups | may cost accuracy |
| Training | reweighting, fairness constraints (Fairlearn ExponentiatedGradient) | explicit accuracy-fairness trade-off |
| Post-processing | group-specific thresholds (Fairlearn ThresholdOptimizer) | uses the attribute at decision time, which is legally restricted in some domains |
| Process | human review for affected groups; restrict use | operational cost |
Record the option chosen, the measured effect (before and after slice tables) and the residual gap.
Tools
slice_metrics.py(this skill): stdlib, CIs, gaps, flags.- Fairlearn (
MetricFrame, mitigation algorithms), AIF360, the What-If Tool, and Evidently for monitoring slices in production.
from fairlearn.metrics import MetricFrame, true_positive_rate, selection_rate
mf = MetricFrame(metrics={"tpr": true_positive_rate, "selection": selection_rate},
y_true=y, y_pred=(score >= 0.4), sensitive_features=df["group"])
mf.by_group, mf.difference(), mf.ratio()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.