2378 3229 TEST73cd812e51sklearn.ensemble._bagging.BaggingClassifier(estimator=sklearn.ensemble._bagging.BaggingClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)) sklearn.BaggingClassifier sklearn.ensemble._bagging.BaggingClassifier 1 openml==0.16.0,sklearn==1.5.2 A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. 2026-02-11T18:28:43 English sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 bootstrap bool true Whether samples are drawn with replacement. If False, sampling without replacement is performed bootstrap_features bool false Whether features are drawn with replacement estimator object {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}} The base estimator to fit on random subsets of the dataset If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeClassifier` .. versionadded:: 1.2 `base_estimator` was renamed to `estimator` max_features int or float 1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details) - If int, then draw `max_features` features - If float, then draw `max(1, int(max_features * n_features_in_))` features max_samples int or float 1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details) - If int, then draw `max_samples` samples - If float, then draw `max_samples * X.shape[0]` samples n_estimators int 10 The number of base estimators in the ensemble n_jobs int null The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details oob_score bool false Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True random_state int null Controls the random resampling of the original dataset (sample wise and feature wise) If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble Pass an int for reproducible output across multiple function calls See :term:`Glossary <random_state>` verbose int 0 Controls the verbosity when fitting and predicting. warm_start bool false When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary <warm_start>` .. versionadded:: 0.17 *warm_start* constructor parameter estimator 2379 3229 TEST73cd812e51sklearn.ensemble._bagging.BaggingClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier) sklearn.BaggingClassifier sklearn.ensemble._bagging.BaggingClassifier 1 openml==0.16.0,sklearn==1.5.2 A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction. Such a meta-estimator can typically be used as a way to reduce the variance of a black-box estimator (e.g., a decision tree), by introducing randomization into its construction procedure and then making an ensemble out of it. This algorithm encompasses several works from the literature. When random subsets of the dataset are drawn as random subsets of the samples, then this algorithm is known as Pasting [1]_. If samples are drawn with replacement, then the method is known as Bagging [2]_. When random subsets of the dataset are drawn as random subsets of the features, then the method is known as Random Subspaces [3]_. Finally, when base estimators are built on subsets of both samples and features, then the method is known as Random Patches [4]_. 2026-02-11T18:28:43 English sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 bootstrap bool true Whether samples are drawn with replacement. If False, sampling without replacement is performed bootstrap_features bool false Whether features are drawn with replacement estimator object {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}} The base estimator to fit on random subsets of the dataset If None, then the base estimator is a :class:`~sklearn.tree.DecisionTreeClassifier` .. versionadded:: 1.2 `base_estimator` was renamed to `estimator` max_features int or float 1.0 The number of features to draw from X to train each base estimator ( without replacement by default, see `bootstrap_features` for more details) - If int, then draw `max_features` features - If float, then draw `max(1, int(max_features * n_features_in_))` features max_samples int or float 1.0 The number of samples to draw from X to train each base estimator (with replacement by default, see `bootstrap` for more details) - If int, then draw `max_samples` samples - If float, then draw `max_samples * X.shape[0]` samples n_estimators int 10 The number of base estimators in the ensemble n_jobs int null The number of jobs to run in parallel for both :meth:`fit` and :meth:`predict`. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details oob_score bool false Whether to use out-of-bag samples to estimate the generalization error. Only available if bootstrap=True random_state int null Controls the random resampling of the original dataset (sample wise and feature wise) If the base estimator accepts a `random_state` attribute, a different seed is generated for each instance in the ensemble Pass an int for reproducible output across multiple function calls See :term:`Glossary <random_state>` verbose int 0 Controls the verbosity when fitting and predicting. warm_start bool false When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new ensemble. See :term:`the Glossary <warm_start>` .. versionadded:: 0.17 *warm_start* constructor parameter estimator 2380 3229 TEST73cd812e51sklearn.tree._classes.DecisionTreeClassifier sklearn.DecisionTreeClassifier sklearn.tree._classes.DecisionTreeClassifier 1 openml==0.16.0,sklearn==1.5.2 A decision tree classifier. 2026-02-11T18:28:43 English sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 ccp_alpha non 0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details .. versionadded:: 0.22 class_weight dict null Weights associated with classes in the form ``{class_label: weight}`` If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}] The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified criterion "gini" max_depth int null The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples max_features int null The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split - If float, then `max_features` is a fraction and `max(1, int(max_features * n_features_in_))` features are considered at each split - If "sqrt", then `max_features=sqrt(n_features)` - If "log2", then `max_features=log2(n_features)` - If None, then `max_features=n_features` Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features max_leaf_nodes int null Grow a tree with ``max_leaf_nodes`` in best-first fashion Best nodes are defined as relative reduction in impurity If None then unlimited number of leaf nodes min_impurity_decrease float 0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed .. versionadded:: 0.19 min_samples_leaf int or float 1 The minimum number of samples required to be at a leaf node A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression - If int, then consider `min_samples_leaf` as the minimum number - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node .. versionchanged:: 0.18 Added float values for fractions min_samples_split int or float 2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split .. versionchanged:: 0.18 Added float values for fractions min_weight_fraction_leaf float 0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided monotonic_cst array null Indicates the monotonicity constraint to enforce on each feature - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If monotonic_cst is None, no constraints are applied Monotonicity constraints are not supported for: - multiclass classifications (i.e. when `n_classes > 2`), - multioutput classifications (i.e. when `n_outputs_ > 1`), - classifications trained on data with missing values The constraints hold over the probability of the positive class Read more in the :ref:`User Guide <monotonic_cst_gbdt>` .. versionadded:: 1.4 random_state int null Controls the randomness of the estimator. The features are always randomly permuted at each split, even if ``splitter`` is set to ``"best"``. When ``max_features < n_features``, the algorithm will select ``max_features`` at random at each split before finding the best split among them. But the best found split may vary across different runs, even if ``max_features=n_features``. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed to an integer See :term:`Glossary <random_state>` for details splitter "best" openml-python python scikit-learn sklearn sklearn_1.5.2 openml-python python scikit-learn sklearn sklearn_1.5.2 openml-python python scikit-learn sklearn sklearn_1.5.2