sklearn.tree.DecisionTreeClassifier |
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criterion{“gini”, “entropy”, “log_loss”}, default=”gini” The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see Mathematical formulation. splitter{“best”, “random”}, default=”best”The 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 split. max_depthint, default=NoneThe 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. min_samples_splitint or float, default=2The 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. Changed in version 0.18: Added float values for fractions. min_samples_leafint or float, default=1The 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. Changed in version 0.18: Added float values for fractions. min_weight_fraction_leaffloat, default=0.0The 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. max_featuresint, float or {“auto”, “sqrt”, “log2”}, default=NoneThe 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. random_stateint, RandomState instance or None, default=NoneControls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features |
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