LightGBM
import lightgbm as lgb
cat_cols = ["merchant_category", "country", "device_type"]
for c in cat_cols:
X_train[c] = X_train[c].astype("category")
X_valid[c] = pd.Categorical(X_valid[c], categories=X_train[c].cat.categories) # same categories, same order
model = lgb.LGBMClassifier(n_estimators=2000, learning_rate=0.05, cat_smooth=10, min_data_per_group=50,
max_cat_threshold=32)
model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], callbacks=[lgb.early_stopping(100)])- Splits by sorting levels by gradient statistics: one split can separate any subset of levels.
- High-cardinality columns can overfit: raise
cat_smooth,min_data_per_group,cat_l2, or switch to target/frequency encoding. - Unseen categories at prediction time follow the "other" direction; NaN is treated as missing.
XGBoost
import xgboost as xgb
for c in cat_cols:
X_train[c] = X_train[c].astype("category")
model = xgb.XGBClassifier(tree_method="hist", enable_categorical=True, max_cat_to_onehot=8,
n_estimators=2000, learning_rate=0.05, early_stopping_rounds=100)
model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)])max_cat_to_onehot: one-hot style splits for columns with fewer levels, partition splits otherwise.- Requires pandas
categorydtype (or a DMatrix with feature types).
scikit-learn HistGradientBoosting
from sklearn.ensemble import HistGradientBoostingClassifier
for c in cat_cols:
X_train[c] = X_train[c].astype("category")
model = HistGradientBoostingClassifier(categorical_features="from_dtype") # sklearn >= 1.4- At most 255 levels per categorical feature (
max_bins); group the tail beforehand. - Inside a Pipeline with an
OrdinalEncoder, passcategorical_featuresas indices or a boolean mask of the encoded columns; negative codes are treated as missing.
CatBoost
from catboost import CatBoostClassifier, Pool
train_pool = Pool(X_train, y_train, cat_features=cat_cols)
valid_pool = Pool(X_valid, y_valid, cat_features=cat_cols)
model = CatBoostClassifier(iterations=3000, learning_rate=0.05, eval_metric="AUC", od_type="Iter", od_wait=200)
model.fit(train_pool, eval_set=valid_pool, use_best_model=True)- Ordered target statistics computed over random permutations (no leakage by design).
- Builds combinations of categorical features automatically (
max_ctr_complexity). - Categorical columns must be strings or integers, not floats; fill NaN with a string like "nan".
- See catboost-model-builder for tuning.
Serving consistency
| Risk | Fix |
|---|---|
| Category order differs between train and serving | save X_train[c].cat.categories with the model; rebuild pd.Categorical(..., categories=saved) |
| New levels in production | they map to NaN/unknown: confirm the model's handling; monitor the unseen rate |
| Different dtypes (int vs str) | normalise to strings before encoding |
| Library version changes | pin versions; re-validate after upgrades |
When native is not the best choice
- Very high cardinality with few rows per level: frequency or cross-fitted target encoding generalises better.
- Linear components or stacked models need numeric encodings anyway.
- Interpretability: one-hot with a linear model, or WoE scorecards, are easier to explain.
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).
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.
counterfactual-explanation-generator
Generate counterfactual explanations and algorithmic recourse: the smallest realistic change to a row's features that changes the model's decision. Solves exact minimal-cost recourse for linear and logistic models (MAD-scaled L1 cost, immutable features, increase/decrease directions, bounds, integer and binary features, diverse alternatives, features every counterfactual must change, and a no-recourse finding when immutable features decide), generates DiCE counterfactuals for any scikit-learn model with constraints turned into permitted ranges and every result re-predicted, and validates counterfactuals from any method for constraint violations, implausible values, off-manifold rows, broken derived-feature relations and failure to flip the prediction.