Overfitting
| Model | Strongest levers first |
|---|---|
| LightGBM | early stopping; min_child_samples up; num_leaves down; feature_fraction/bagging_fraction < 1 (+ bagging_freq); lambda_l2; min_gain_to_split; max_bin down; lower learning rate + more rounds |
| XGBoost | early stopping; max_depth down; min_child_weight up; subsample, colsample_bytree < 1; reg_lambda, gamma (min split loss) up |
| CatBoost | early stopping (od_type="Iter", od_wait); depth down; l2_leaf_reg up; random_strength up; ordered boosting on small data |
| HistGradientBoosting | early_stopping=True; max_leaf_nodes down; min_samples_leaf up; l2_regularization |
| Random forest / extra trees | min_samples_leaf up; max_features down; max_depth; more trees only reduce variance from randomness |
| Decision tree | ccp_alpha (cost-complexity pruning) via the 1-SE rule; max_depth; min_samples_leaf |
| Linear / logistic | lower C / higher alpha; L1 or elastic net to drop features; fewer one-hot levels (min_frequency) |
| SVM | lower C; lower RBF gamma |
| k-NN | larger n_neighbors; feature selection (distance concentration in high dimensions) |
| MLP | early stopping; weight decay; dropout; smaller layers; more data/augmentation |
Feature-side remedies for any model: drop ID-like and high-cardinality columns, target-encode with cross-fitting, remove features that only work on training rows (leakage), aggregate sparse categories.
Underfitting
| Model | Levers |
|---|---|
| Boosting | more rounds (or higher learning rate); num_leaves/max_depth up; smaller min_child_samples; less L2 |
| Random forest | min_samples_leaf=1; higher max_features |
| Linear / logistic | splines (SplineTransformer), interactions, log/ratio transforms, weaker penalty, or a tree model |
| SVM | higher C, higher gamma |
| k-NN | smaller n_neighbors, scaled features |
| Any | new informative features; check label noise (inter-annotator agreement); check the target definition |
Early-stopping set-ups
# LightGBM
m = lgb.LGBMClassifier(n_estimators=10000, learning_rate=0.03)
m.fit(X_tr, y_tr, eval_set=[(X_va, y_va)], callbacks=[lgb.early_stopping(100), lgb.log_evaluation(0)])
# XGBoost (>= 2.0: constructor argument)
m = xgb.XGBClassifier(n_estimators=10000, learning_rate=0.03, early_stopping_rounds=100)
m.fit(X_tr, y_tr, eval_set=[(X_va, y_va)], verbose=False)
# CatBoost
m = CatBoostClassifier(iterations=10000, learning_rate=0.03, od_type="Iter", od_wait=100)
m.fit(X_tr, y_tr, eval_set=(X_va, y_va), use_best_model=True)
# scikit-learn
HistGradientBoostingClassifier(early_stopping=True, validation_fraction=0.1, n_iter_no_change=20)- The early-stopping set must not be the test set; inside CV use each fold's validation split, then refit on all data with about the mean best iteration x (1 + 1/k) for k folds.
- Patience: about 5-10% of the expected rounds, never below 10 for noisy metrics.
- Stop on the metric you care about (
first_metric_only=Truein LightGBM when several are logged).
Confirming a fix
| Fix | Curve that confirms it |
|---|---|
| Regularisation | gap at the best iteration shrinks, best validation unchanged or better |
| Early stopping | final validation = best validation; fewer rounds |
| More capacity | training score up, validation up; the gap may widen a little |
| New features | validation up at the same complexity; the validation curve's peak moves up |
| Simpler setting via 1-SE | holdout score within noise of the best, model smaller/faster |
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.