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name: estimators-and-ensembles description: "Use mlxtend sklearn-style classifiers, regressors, Kmeans, voting ensembles, and stacking meta-estimators." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

estimators-and-ensembles

Use this sub-skill when a task needs to construct, fit, tune, or diagnose mlxtend.classifier, mlxtend.regressor, or mlxtend.cluster estimators and ensemble meta-estimators.

Read first

• references/api-reference.md for verified constructor signatures, estimator methods, sample-weight behavior, meta-feature attributes, and sklearn parameter names.
• references/workflows.md for concrete recipes covering soft/hard voting, stacking classification, stacking regression, classic iterative estimators, linear regression, and Kmeans.
• references/troubleshooting.md for predict_proba, sample_weight, GridSearchCV, unfitted/shape, label, convergence, and learning-rate failures.
• scripts/estimator_ensemble_smoke.py to smoke-test installed estimator APIs on deterministic tiny CPU data.

Route here for

• EnsembleVoteClassifier hard/soft voting, voting weights, prefit base estimators, sklearn GridSearchCV, and classifier probability aggregation.
• StackingClassifier and StackingCVClassifier, including use_probas, drop_proba_col, average_probas, use_features_in_secondary, store_train_meta_features, cross-validation, groups, clone/refit decisions, and meta-classifier tuning.
• StackingRegressor and StackingCVRegressor, including out-of-fold meta-features, multi-output targets, refit, use_features_in_secondary, and prefixed base/meta regressor parameters.
• Classic mlxtend estimators: Adaline, Perceptron, LogisticRegression, SoftmaxRegression, MultiLayerPerceptron, OneRClassifier, LinearRegression, and Kmeans.
• sklearn-style decisions around fit, predict, predict_proba, predict_meta_features, get_params, set_params, cross_val_score, and GridSearchCV.

Boundaries and sibling routes

• Do not decide evaluation metrics, statistical model-comparison tests, bootstrap procedures, or validation splitters here except as minimal estimator examples. For those, read ../evaluation-and-validation/SKILL.md.
• Do not cover feature selectors, preprocessing transformers, PCA/LDA/kernel PCA, TransactionEncoder, or column-selection pipelines in depth. For those, read ../feature-workflows/SKILL.md.
• Do not cover decision-region plots, confusion-matrix plots, learning curves, or Matplotlib utility behavior here. For visualization and utilities, read ../plotting-and-utilities/SKILL.md.

Operating reminders

• Prefer sklearn estimators for base learners in ensembles unless you specifically need mlxtend's educational iterative implementations; clone-compatible sklearn estimators make GridSearchCV and cross-validation safer.
• For soft voting or probability-based stacking, every probability-producing base estimator used for meta-features must implement predict_proba; the final meta-classifier must implement predict_proba only if the downstream call needs probabilities.
• Use StackingCVClassifier or StackingCVRegressor when avoiding training-set leakage in meta-features matters; standard stacking fits level-2 models on predictions from base models trained on the same data.
• Keep task-specific scoring, plotting, and feature engineering in sibling sub-skills; this sub-skill owns estimator construction, fitting, prediction, and sklearn integration decisions.