{"flow":{"id":"18687","uploader":"11601","name":"sklearn.ensemble._forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble._forest.RandomForestClassifier","version":"5","external_version":"openml==0.10.2,sklearn==0.23.2","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 uses averaging to\nimprove the predictive accuracy and control over-fitting.\nThe sub-sample size is controlled with the `max_samples` parameter if\n`bootstrap=True` (default), otherwise the whole dataset is used to build\neach tree.","upload_date":"2020-09-25T00:48:34","language":"English","dependencies":"sklearn==0.23.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"bootstrap","data_type":"bool","default_value":"true","description":"Whether bootstrap samples are used when building trees. If False, the\n whole dataset is used to build each tree"},{"name":"ccp_alpha","data_type":"non","default_value":"0.0","description":"Complexity parameter used for Minimal Cost-Complexity Pruning. The\n subtree with the largest cost complexity that is smaller than\n ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n :ref:`minimal_cost_complexity_pruning` for details\n\n .. versionadded:: 0.22"},{"name":"class_weight","data_type":[],"default_value":"null","description":[]},{"name":"criterion","data_type":[],"default_value":"\"gini\"","description":[]},{"name":"max_depth","data_type":"int","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":[],"default_value":"\"auto\"","description":[]},{"name":"max_leaf_nodes","data_type":"int","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":"max_samples","data_type":"int or float","default_value":"null","description":"If bootstrap is True, the number of samples to draw from X\n to train each base estimator\n\n - If None (default), then draw `X.shape[0]` samples\n - If int, then draw `max_samples` samples\n - If float, then draw `max_samples * X.shape[0]` samples. Thus,\n `max_samples` should be in the interval `(0, 1)`\n\n .. versionadded:: 0.22"},{"name":"min_impurity_decrease","data_type":"float","default_value":"0.0","description":"A node will be split if this split induces a decrease of the impurity\n greater than or equal to this value\n\n The weighted impurity decrease equation is the following::\n\n N_t \/ N * (impurity - N_t_R \/ N_t * right_impurity\n - N_t_L \/ N_t * left_impurity)\n\n where ``N`` is the total number of samples, ``N_t`` is the number of\n samples at the current node, ``N_t_L`` is the number of samples in the\n left child, and ``N_t_R`` is the number of samples in the right child\n\n ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n if ``sample_weight`` is passed\n\n .. versionadded:: 0.19"},{"name":"min_impurity_split","data_type":"float","default_value":"null","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 .. deprecated:: 0.19\n ``min_impurity_split`` has been deprecated in favor of\n ``min_impurity_decrease`` in 0.19. The default value of\n ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it\n will be removed in 0.25. Use ``min_impurity_decrease`` instead"},{"name":"min_samples_leaf","data_type":"int or float","default_value":"1","description":"The minimum number of samples required to be at a leaf node\n A split point at any depth will only be considered if it leaves at\n least ``min_samples_leaf`` training samples in each of the left and\n right branches. This may have the effect of smoothing the model,\n especially in regression\n\n - If int, then consider `min_samples_leaf` as the minimum number\n - If float, then `min_samples_leaf` is a fraction 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 fractions"},{"name":"min_samples_split","data_type":"int or float","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 fraction 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 fractions"},{"name":"min_weight_fraction_leaf","data_type":"float","default_value":"0.0","description":"The minimum weighted fraction of the sum total of weights (of all\n the input samples) required to be at a leaf node. Samples have\n equal weight when sample_weight is not provided\n\nmax_features : {\"auto\", \"sqrt\", \"log2\"}, int or float, default=\"auto\"\n 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 fraction 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":"n_estimators","data_type":"int","default_value":"100","description":"The number of trees in the forest\n\n .. versionchanged:: 0.22\n The default value of ``n_estimators`` changed from 10 to 100\n in 0.22\n\ncriterion : {\"gini\", \"entropy\"}, default=\"gini\"\n 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":"n_jobs","data_type":"int","default_value":"null","description":"The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n :meth:`decision_path` and :meth:`apply` are all parallelized over the\n trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n context. ``-1`` means using all processors. See :term:`Glossary\n ` for more details"},{"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 or RandomState","default_value":"null","description":"Controls both the randomness of the bootstrapping of the samples used\n when building trees (if ``bootstrap=True``) and the sampling of the\n features to consider when looking for the best split at each node\n (if ``max_features < n_features``)\n See :term:`Glossary ` for details"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity when fitting and predicting"},{"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. See :term:`the Glossary `\n\nclass_weight : {\"balanced\", \"balanced_subsample\"}, dict or list of dicts, 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 Note that for multioutput (including multilabel) weights should be\n defined for each class of every column in its own dict. For example,\n for four-class multilabel classification weights should be\n [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n [{1:1}, {2:5}, {3:1}, {4:1}]\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_cl..."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.23.2"]}}