{"flow":{"id":"18689","uploader":"3821","name":"sklearn.ensemble._gb.GradientBoostingClassifier","custom_name":"sklearn.GradientBoostingClassifier","class_name":"sklearn.ensemble._gb.GradientBoostingClassifier","version":"2","external_version":"openml==0.10.2,sklearn==0.23.1","description":"Gradient Boosting for classification.\n\nGB builds an additive model in a\nforward stage-wise fashion; it allows for the optimization of\narbitrary differentiable loss functions. In each stage ``n_classes_``\nregression trees are fit on the negative gradient of the\nbinomial or multinomial deviance loss function. Binary classification\nis a special case where only a single regression tree is induced.","upload_date":"2020-09-29T22:07:55","language":"English","dependencies":"sklearn==0.23.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"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":"criterion","data_type":[],"default_value":"\"friedman_mse\"","description":[]},{"name":"init","data_type":"estimator or","default_value":"null","description":"An estimator object that is used to compute the initial predictions\n ``init`` has to provide :meth:`fit` and :meth:`predict_proba`. If\n 'zero', the initial raw predictions are set to zero. By default, a\n ``DummyEstimator`` predicting the classes priors is used"},{"name":"learning_rate","data_type":"float","default_value":"0.1","description":"learning rate shrinks the contribution of each tree by `learning_rate`\n There is a trade-off between learning_rate and n_estimators"},{"name":"loss","data_type":[],"default_value":"\"deviance\"","description":[]},{"name":"max_depth","data_type":"int","default_value":"3","description":"maximum depth of the individual regression estimators. The maximum\n depth limits the number of nodes in the tree. Tune this parameter\n for best performance; the best value depends on the interaction\n of the input variables"},{"name":"max_features","data_type":[],"default_value":"null","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":"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"},{"name":"n_estimators","data_type":"int","default_value":"100","description":"The number of boosting stages to perform. Gradient boosting\n is fairly robust to over-fitting so a large number usually\n results in better performance"},{"name":"n_iter_no_change","data_type":"int","default_value":"null","description":"``n_iter_no_change`` is used to decide if early stopping will be used\n to terminate training when validation score is not improving. By\n default it is set to None to disable early stopping. If set to a\n number, it will set aside ``validation_fraction`` size of the training\n data as validation and terminate training when validation score is not\n improving in all of the previous ``n_iter_no_change`` numbers of\n iterations. The split is stratified\n\n .. versionadded:: 0.20"},{"name":"presort","data_type":"deprecated","default_value":"\"deprecated\"","description":"This parameter is deprecated and will be removed in v0.24\n\n .. deprecated :: 0.22"},{"name":"random_state","data_type":"int or RandomState","default_value":"null","description":"Controls the random seed given to each Tree estimator at each\n boosting iteration\n In addition, it controls the random permutation of the features at\n each split (see Notes for more details)\n It also controls the random spliting of the training data to obtain a\n validation set if `n_iter_no_change` is not None\n Pass an int for reproducible output across multiple function calls\n See :term:`Glossary `\n\nmax_features : {'auto', 'sqrt', 'log2'}, int or float, default=None\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)`\n - If 'log2', then `max_features=log2(n_features)`\n - If None, then `max_features=n_features`\n\n Choosing `max_features < ..."},{"name":"subsample","data_type":"float","default_value":"1.0","description":"The fraction of samples to be used for fitting the individual base\n learners. If smaller than 1.0 this results in Stochastic Gradient\n Boosting. `subsample` interacts with the parameter `n_estimators`\n Choosing `subsample < 1.0` leads to a reduction of variance\n and an increase in bias\n\ncriterion : {'friedman_mse', 'mse', 'mae'}, default='friedman_mse'\n The function to measure the quality of a split. Supported criteria\n are 'friedman_mse' for the mean squared error with improvement\n score by Friedman, 'mse' for mean squared error, and 'mae' for\n the mean absolute error. The default value of 'friedman_mse' is\n generally the best as it can provide a better approximation in\n some cases\n\n .. versionadded:: 0.18"},{"name":"tol","data_type":"float","default_value":"0.0001","description":"Tolerance for the early stopping. When the loss is not improving\n by at least tol for ``n_iter_no_change`` iterations (if set to a\n number), the training stops\n\n .. versionadded:: 0.20"},{"name":"validation_fraction","data_type":"float","default_value":"0.1","description":"The proportion of training data to set aside as validation set for\n early stopping. Must be between 0 and 1\n Only used if ``n_iter_no_change`` is set to an integer\n\n .. versionadded:: 0.20"},{"name":"verbose","data_type":"int","default_value":"0","description":"Enable verbose output. If 1 then it prints progress and performance\n once in a while (the more trees the lower the frequency). If greater\n than 1 then it prints progress and performance for every tree"},{"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 erase the\n previous solution. See :term:`the Glossary `"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.23.1"]}}