{"flow":{"id":"18864","uploader":"6691","name":"sklearn.ensemble.forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble.forest.RandomForestClassifier","version":"66","external_version":"openml==0.12.2,sklearn==0.18","description":"A random forest classifier.\n\nA random forest is a meta estimator that fits a number of decision tree\nclassifiers on various sub-samples of the dataset and use averaging to\nimprove the predictive accuracy and control over-fitting.\nThe sub-sample size is always the same as the original\ninput sample size but the samples are drawn with replacement if\n`bootstrap=True` (default).","upload_date":"2021-08-13T18:08:58","language":"English","dependencies":"sklearn==0.18\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"bootstrap","data_type":"boolean","default_value":"true","description":"Whether bootstrap samples are used when building trees"},{"name":"class_weight","data_type":"dict","default_value":"null","description":"\"balanced_subsample\" or None, optional (default=None)\n Weights associated with classes in the form ``{class_label: weight}``\n If not given, all classes are supposed to have weight one. For\n multi-output problems, a list of dicts can be provided in the same\n order as the columns of y\n\n The \"balanced\" mode uses the values of y to automatically adjust\n weights inversely proportional to class frequencies in the input data\n as ``n_samples \/ (n_classes * np.bincount(y))``\n\n The \"balanced_subsample\" mode is the same as \"balanced\" except that\n weights are computed based on the bootstrap sample for every tree\n grown\n\n For multi-output, the weights of each column of y will be multiplied\n\n Note that these weights will be multiplied with sample_weight (passed\n through the fit method) if sample_weight is specified."},{"name":"criterion","data_type":"string","default_value":"\"gini\"","description":"The function to measure the quality of a split. Supported criteria are\n \"gini\" for the Gini impurity and \"entropy\" for the information gain\n Note: this parameter is tree-specific"},{"name":"max_depth","data_type":"integer or None","default_value":"null","description":"The maximum depth of the tree. If None, then nodes are expanded until\n all leaves are pure or until all leaves contain less than\n min_samples_split samples"},{"name":"max_features","data_type":"int","default_value":"\"auto\"","description":"The number of features to consider when looking for the best split:\n\n - If int, then consider `max_features` features at each split\n - If float, then `max_features` is a percentage and\n `int(max_features * n_features)` features are considered at each\n split\n - If \"auto\", then `max_features=sqrt(n_features)`\n - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\")\n - If \"log2\", then `max_features=log2(n_features)`\n - If None, then `max_features=n_features`\n\n Note: the search for a split does not stop until at least one\n valid partition of the node samples is found, even if it requires to\n effectively inspect more than ``max_features`` features"},{"name":"max_leaf_nodes","data_type":"int or None","default_value":"null","description":"Grow trees with ``max_leaf_nodes`` in best-first fashion\n Best nodes are defined as relative reduction in impurity\n If None then unlimited number of leaf nodes"},{"name":"min_impurity_split","data_type":"float","default_value":"1e-07","description":"Threshold for early stopping in tree growth. A node will split\n if its impurity is above the threshold, otherwise it is a leaf\n\n .. versionadded:: 0.18"},{"name":"min_samples_leaf","data_type":"int","default_value":"1","description":"The minimum number of samples required to be at a leaf node:\n\n - If int, then consider `min_samples_leaf` as the minimum number\n - If float, then `min_samples_leaf` is a percentage and\n `ceil(min_samples_leaf * n_samples)` are the minimum\n number of samples for each node\n\n .. versionchanged:: 0.18\n Added float values for percentages"},{"name":"min_samples_split","data_type":"int","default_value":"2","description":"The minimum number of samples required to split an internal node:\n\n - If int, then consider `min_samples_split` as the minimum number\n - If float, then `min_samples_split` is a percentage and\n `ceil(min_samples_split * n_samples)` are the minimum\n number of samples for each split\n\n .. versionchanged:: 0.18\n Added float values for percentages"},{"name":"min_weight_fraction_leaf","data_type":"float","default_value":"0.0","description":"The minimum weighted fraction of the input samples required to be at a\n leaf node"},{"name":"n_estimators","data_type":"integer","default_value":"10","description":"The number of trees in the forest"},{"name":"n_jobs","data_type":"integer","default_value":"1","description":"The number of jobs to run in parallel for both `fit` and `predict`\n If -1, then the number of jobs is set to the number of cores"},{"name":"oob_score","data_type":"bool","default_value":"false","description":"Whether to use out-of-bag samples to estimate\n the generalization accuracy"},{"name":"random_state","data_type":"int","default_value":"null","description":"If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity of the tree building process"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to ``True``, reuse the solution of the previous call to fit\n and add more estimators to the ensemble, otherwise, just fit a whole\n new forest"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.18"]}}