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sklearn.tree.tree.ExtraTreeClassifier

sklearn.tree.tree.ExtraTreeClassifier

Visibility: public Uploaded 07-11-2019 by Test Test sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 252 runs
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  • openml-python python scikit-learn sklearn sklearn_0.21.2
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An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups, random splits are drawn for each of the `max_features` randomly selected features and the best split among those is chosen. When `max_features` is set 1, this amounts to building a totally random decision tree. Warning: Extra-trees should only be used within ensemble methods.

Parameters

class_weightWeights associated with classes in the form ``{class_label: weight}`` If not given, 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 See also -------- ExtraTreeRegressor, sklearn.ensemble.ExtraTreesC...default: null
criterionThe function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gaindefault: "gini"
max_depthThe 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 samplesdefault: null
max_featuresThe 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`` featuresdefault: "auto"
max_leaf_nodesGrow 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 nodesdefault: null
min_impurity_decreaseA 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:: 0.19default: 0.0
min_impurity_splitThreshold 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`` insteaddefault: null
min_samples_leafThe 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 fractionsdefault: 1
min_samples_splitThe 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 fractionsdefault: 2
min_weight_fraction_leafThe 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 provideddefault: 0.0
random_stateIf 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`default: null
splitterThe strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random splitdefault: "random"

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