Exact solution for linear scores
For a score z = b + sum_j w_j x_j (logistic: probability = sigmoid(z)), reaching a target z* from z0
needs a score change need = |z* - z0| in one direction. With cost sum_j |delta_j| / s_j x c_j:
minimise sum_j |delta_j| x c_j / s_j
subject to sum_j w_j delta_j moves z by `need` in the wanted direction
delta_j within [lo_j, hi_j] (immutability, direction, bounds)This is a fractional knapsack. Use features in decreasing order of efficiency |w_j| x s_j / c_j (score
change per unit cost) until the need is covered. This is optimal for continuous features.
linear_counterfactual.py adds:
- exhaustive search over binary features (2^b subsets, small b)
- floor/ceil branching for integer features (exact; checked against brute force)
- alternatives: the best solution with each used feature excluded, and single-feature solutions
- essential features: excluding them leaves no feasible solution
- NO RECOURSE: the best reachable score when even every allowed move is not enough
L1 cost gives sparse answers that change few features. L2 cost spreads small changes over many features, which is harder to act on.
Wachter et al. (2017)
argmin_x' lambda x (f(x') - y*)^2 + d(x, x') (d = MAD-weighted L1)Gradient-based. It needs a differentiable model, and constraints come through projection.
alibi.explainers.Counterfactual implements it.
DiCE (Mothilal et al. 2020)
Generates several counterfactuals that trade off proximity, diversity (a determinantal point process term) and sparsity.
import dice_ml
data = dice_ml.Data(dataframe=train, continuous_features=cont, outcome_name="churned")
model = dice_ml.Model(model=pipe, backend="sklearn", model_type="classifier")
exp = dice_ml.Dice(data, model, method="random") # "genetic", "kdtree"
cf = exp.generate_counterfactuals(query, total_CFs=4, desired_class="opposite",
features_to_vary=[...], permitted_range={"monthly_charges": [20, 84.15]},
random_seed=0) # random_seed: method="random" only (verified on dice-ml 0.12)
cf.cf_examples_list[0].final_cfs_df| method | Behaviour |
|---|---|
| random | random sampling over the features to vary: fast and diverse, not minimal |
| genetic | evolutionary search with proximity/sparsity terms: closer, slower |
| kdtree | nearest training rows of the desired class: plausible by construction, may change many features |
DiCE has no direction constraints. Express them as per-row permitted_range (gen_dice_script.py does).
Categoricals are strings inside DiCE, so wrap the model to restore dtypes. Re-predict every result.
Prototype-guided (Van Looveren and Klaise 2021)
alibi.explainers.CounterfactualProto pulls the counterfactual towards a class prototype (autoencoder or
k-d tree), which improves plausibility and is faster for complex models.
MILP / actionable recourse (Ustun, Spangher and Liu 2019)
Exact recourse for linear models (and, with encodings, tree ensembles) with rich constraints. It can
audit a population: the share of rejected people with no recourse, per group. Package: recourse
(actionable-recourse).
Choosing
| Need | Method |
|---|---|
| Logistic scorecard, exact and explainable | linear solver / MILP |
| Any sklearn model, several options quickly | DiCE random, then validate |
| Plausibility over minimality | DiCE kdtree or prototypes |
| Audit recourse fairness across many rows | linear solver / MILP in a loop, per-group statistics |
| Tree ensembles, exact | MILP encodings (e.g. FOCUS, FACE, or recourse with tree support) |
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.