Why LIME varies
- Sampling: each run draws new perturbations. With few effective samples (small kernel width, many features), the weighted regression is noisy.
- Feature selection: with
num_features< all features, borderline features enter in some runs and not in others. - Discretisation: a row near a quartile boundary sits in a bin with very different neighbours.
- Kernel width: different widths answer different questions. The default is arbitrary.
Alvarez-Melis and Jaakkola (2018) showed that LIME explanations can change substantially for nearby inputs. Slack et al. (2020) showed that adversarial models can hide behaviour from LIME's perturbations, because the perturbed samples are off the data manifold. Stability checks therefore matter.
The checks in lime_stability.py
| Quantity | Definition |
|---|---|
| mean, std | weight across runs (0 when the feature is not selected in a run) |
| mean |weight| | used for ordering and for "negligible" (< 10% of the largest), so sign flips do not hide a feature |
| CV | std / |mean|: size reliability |
| sign consistency | share of non-zero runs agreeing with the majority sign |
| top-k share | share of runs with the feature in the top k by |weight| |
| top-k Jaccard | mean pairwise |A intersect B| / |A union B| of top-k sets, negligible features removed |
| fidelity | mean exp.score (weighted R^2 of the surrogate) |
| local gap | mean |local_pred - model_pred| |
Troubleshooting
| Symptom | Try |
|---|---|
| UNSTABLE, low Jaccard | --samples 20000; fewer features (--top 5); a slightly larger kernel width |
| Sign flips on one feature | that feature's local effect is near zero or the row sits on a bin edge: report it as uncertain |
| Low fidelity (R^2 < 0.5) | --no-discretize; a smaller kernel width; or the model is very non-linear here, so use SHAP |
| Large local gap | the surrogate misses the model at the row itself: do not report |
| Disagreement with SHAP | different questions (local slope vs attribution relative to a baseline); trust the stable, faithful one and explain the difference |
| Very different explanations for similar rows | the model is locally unstable, or LIME is: check both with SHAP |
LIME vs SHAP on the same row
They answer different questions:
- LIME: what linear function approximates the model near this row? Weights are local slopes per rule.
- SHAP: how much does each feature contribute to moving the prediction from the baseline to this row? The values are additive.
Sign agreement on the main features is expected. Disagreement often comes from non-linearity: a feature can
push the prediction up relative to the baseline (positive SHAP) while its local slope is flat or negative
(LIME). lime_stability.py --compare shap_row.csv lists the disagreeing features.
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.