432 1159 TEST719f06ea92sklearn.ensemble._voting.VotingClassifier(dt=sklearn.tree._classes.DecisionTreeClassifier) sklearn.VotingClassifier sklearn.ensemble._voting.VotingClassifier 1 openml==0.14.1,sklearn==0.22.2 Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17 2024-01-10T15:42:39 English sklearn==0.22.2 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 estimators list of [{"oml-python:serialized_object": "component_reference", "value": {"key": "dt", "step_name": "dt"}}] Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones of those original estimators that will be stored in the class attribute ``self.estimators_``. An estimator can be set to ``'drop'`` using ``set_params`` .. deprecated:: 0.22 Using ``None`` to drop an estimator is deprecated in 0.22 and support will be dropped in 0.24. Use the string ``'drop'`` instead flatten_transform bool true Affects shape of transform output only when voting='soft' If voting='soft' and flatten_transform=True, transform method returns matrix with shape (n_samples, n_classifiers * n_classes). If flatten_transform=False, it returns (n_classifiers, n_samples, n_classes). n_jobs int or None null The number of jobs to run in parallel for ``fit`` ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details voting str "hard" If 'hard', uses predicted class labels for majority rule voting Else if 'soft', predicts the class label based on the argmax of the sums of the predicted probabilities, which is recommended for an ensemble of well-calibrated classifiers weights array null Sequence of weights (`float` or `int`) to weight the occurrences of predicted class labels (`hard` voting) or class probabilities before averaging (`soft` voting). Uses uniform weights if `None` dt 423 1159 TEST719f06ea92sklearn.tree._classes.DecisionTreeClassifier sklearn.DecisionTreeClassifier sklearn.tree._classes.DecisionTreeClassifier 1 openml==0.14.1,sklearn==0.22.2 A decision tree classifier. 2024-01-10T15:42:36 English sklearn==0.22.2 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 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 2 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 `int(max_features * n_features)` features are considered at each split - If "auto", then `max_features=sqrt(n_features)` - 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_impurity_split float null Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead 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 presort deprecated "deprecated" This parameter is deprecated and will be removed in v0.24 .. deprecated:: 0.22 random_state int or RandomState null If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random` splitter "best" openml-python python scikit-learn sklearn sklearn_0.22.2 openml-python python scikit-learn sklearn sklearn_0.22.2