:orphan: .. _api: APIs **** ============ Main modules ============ ~~~~~~~~~~~~~~ Classification ~~~~~~~~~~~~~~ .. autoclass:: autosklearn.classification.AutoSklearnClassifier :members: :inherited-members: show_models, fit_ensemble, refit, sprint_statistics .. autoclass:: autosklearn.experimental.askl2.AutoSklearn2Classifier :inherited-members: show_models, fit_ensemble, refit, sprint_statistics, fit, predict, predict_proba ~~~~~~~~~~ Regression ~~~~~~~~~~ .. autoclass:: autosklearn.regression.AutoSklearnRegressor :members: :inherited-members: show_models, fit_ensemble, refit, sprint_statistics ======= Metrics ======= .. autofunction:: autosklearn.metrics.make_scorer ~~~~~~~~~~~~~~~~ Built-in Metrics ~~~~~~~~~~~~~~~~ Classification metrics ~~~~~~~~~~~~~~~~~~~~~~ Note: The default ``autosklearn.metrics.f1``, ``autosklearn.metrics.precision`` and ``autosklearn.metrics.recall`` built-in metrics are applicable only for binary classification. In order to apply them on multilabel and multiclass classification, please use the corresponding metrics with an appropriate averaging mechanism, such as ``autosklearn.metrics.f1_macro``. For more information about how these metrics are used, please read `this scikit-learn documentation `_. .. autoclass:: autosklearn.metrics.accuracy .. autoclass:: autosklearn.metrics.balanced_accuracy .. autoclass:: autosklearn.metrics.f1 .. autoclass:: autosklearn.metrics.f1_macro .. autoclass:: autosklearn.metrics.f1_micro .. autoclass:: autosklearn.metrics.f1_samples .. autoclass:: autosklearn.metrics.f1_weighted .. autoclass:: autosklearn.metrics.roc_auc .. autoclass:: autosklearn.metrics.precision .. autoclass:: autosklearn.metrics.precision_macro .. autoclass:: autosklearn.metrics.precision_micro .. autoclass:: autosklearn.metrics.precision_samples .. autoclass:: autosklearn.metrics.precision_weighted .. autoclass:: autosklearn.metrics.average_precision .. autoclass:: autosklearn.metrics.recall .. autoclass:: autosklearn.metrics.recall_macro .. autoclass:: autosklearn.metrics.recall_micro .. autoclass:: autosklearn.metrics.recall_samples .. autoclass:: autosklearn.metrics.recall_weighted .. autoclass:: autosklearn.metrics.log_loss Regression metrics ~~~~~~~~~~~~~~~~~~ .. autoclass:: autosklearn.metrics.r2 .. autoclass:: autosklearn.metrics.mean_squared_error .. autoclass:: autosklearn.metrics.mean_absolute_error .. autoclass:: autosklearn.metrics.median_absolute_error ==================== Extension Interfaces ==================== .. autoclass:: autosklearn.pipeline.components.base.AutoSklearnClassificationAlgorithm :members: .. autoclass:: autosklearn.pipeline.components.base.AutoSklearnRegressionAlgorithm :members: .. autoclass:: autosklearn.pipeline.components.base.AutoSklearnPreprocessingAlgorithm :members: .. _api_ensemble: ========= Ensembles ========= ~~~~~~~~~~~~~~~~ Single objective ~~~~~~~~~~~~~~~~ .. autoclass:: autosklearn.ensembles.EnsembleSelection :members: Single model classes ~~~~~~~~~~~~~~~~~~~~ These classes wrap a single model to provide a unified interface in Auto-sklearn. .. autoclass:: autosklearn.ensembles.SingleBest :members: .. autoclass:: autosklearn.ensembles.SingleModelEnsemble :members: .. autoclass:: autosklearn.ensembles.SingleBestFromRunhistory :members: ~~~~~~~~~~~~~~~ Multi-objective ~~~~~~~~~~~~~~~ .. autoclass:: autosklearn.ensembles.MultiObjectiveDummyEnsemble :members: