Ribeiro, Singh and Guestrin (2016), "Why should I trust you?". The lime package, lime_tabular module.
Algorithm
given row x, model f, training data T:
1. draw N perturbed samples z around x:
continuous features (discretize_continuous=True): pick a quartile bin from the training distribution,
then a value inside the bin; the surrogate sees a 0/1 "same bin as x" indicator
continuous features (discretize_continuous=False): z = x + noise scaled by the training std
categorical features: draw a level with the training frequencies; the surrogate sees 1 if equal to x's level
2. query the model: f(z) (the probability of the explained class, or the regression output)
3. weight each sample by proximity: w = exp(-d(x, z)^2 / kernel_width^2), d = Euclidean on scaled features
4. select K features (forward selection / highest weights / lasso path)
5. fit weighted ridge regression f(z) ~ features of z; the coefficients are the explanation
6. score = weighted R^2 of that fit (fidelity); local_pred = the surrogate's prediction at xCalls
from lime.lime_tabular import LimeTabularExplainer
explainer = LimeTabularExplainer(
X_train_encoded, feature_names=cols, categorical_features=cat_idx,
categorical_names={i: levels for i, levels in ...}, class_names=["stay", "churn"],
mode="classification", discretize_continuous=True, discretizer="quartile",
kernel_width=None, random_state=0)
exp = explainer.explain_instance(x_encoded, predict_fn, num_features=8, num_samples=5000, labels=(1,))
exp.as_list(label=1), exp.score, exp.local_pred, exp.intercept
exp.save_to_file("row.html")predict_fnreceives a NumPy array in the encoded space and must return class probabilities (classification) or predictions (regression). Decode categorical codes back to the original values before calling a Pipeline. The generated script does this.random_stateis read at construction. To repeat with new seeds, setexplainer.random_state = np.random.RandomState(seed)before eachexplain_instance.discretizercan be"quartile","decile"or"entropy"(the last needs labels).feature_selection:"auto"(default),"forward_selection","lasso_path","highest_weights","none".
Kernel width
The default is 0.75 x sqrt(n_features) on standardised distances. Smaller widths make the explanation more
local but noisier, because fewer samples carry weight. Larger widths approach a global linear model. Try 2-3
values and keep the one with acceptable fidelity and stability, then use it consistently across rows.
Cost
N model evaluations per explanation (5000 by default) and per repeat. Batch predictions are vectorised, so a fast model takes seconds per row. For an external API, cache predictions and reduce N.
Variants
| Variant | Idea |
|---|---|
Anchors (Ribeiro 2018, alibi.explainers.AnchorTabular) | if-then rules with a precision guarantee: "if support_calls > 3 and plan = basic, the prediction is churn with 95% precision" |
| SP-LIME (submodular pick) | choose a few representative rows to explain the model globally |
| BayLIME / stable LIME variants | reduce sampling variance |
| KernelSHAP | LIME with the Shapley kernel: additive, consistent, slower |
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.