scikitlearncompatible

Scikitlearncompatible refers to software components, models, or libraries that adhere to the scikit-learn estimator API, enabling seamless use within the scikit-learn ecosystem. This compatibility allows these components to be invoked in the same ways as native scikit-learn estimators, including via pipelines, cross-validation, grid search, and ensemble methods.

An essential feature of scikitlearncompatible objects is their API surface. They typically implement methods such as fit(X, y=None), transform(X), predict(X), predict_proba(X) or decision_function(X), and score(X, y). They also support parameter introspection through get_params and set_params, facilitating hyperparameter tuning and reproducibility. Many authors achieve compatibility by subclassing common mixins such as BaseEstimator, TransformerMixin, ClassifierMixin, or RegressorMixin, which provide standard behaviors and parameter handling.

Interoperability is a core motivator for scikitlearncompatibility. When a component conforms to the API, it can be used in scikit-learn pipelines, alongside standard transformers and estimators, and can participate in tools like GridSearchCV, RandomizedSearchCV, and cross_val_score. Validation utilities such as estimator_checks.check_estimator can be used to verify compatibility and catch API deviations.

Examples of scikitlearncompatible components include wrappers around models from other frameworks that expose a scikit-learn-like interface, as well as custom estimators designed to plug into scikit-learn workflows. While the API is designed to be flexible, authors must document any deviations or limitations, such as specialized input formats or behavior differences in fit or predict methods.

See also: scikit-learn estimator API, pipeline, estimator checks, parameter tuning.