17437 1 sklearn.ensemble.gradient_boosting.GradientBoostingClassifier sklearn.GradientBoostingClassifier sklearn.ensemble.gradient_boosting.GradientBoostingClassifier 24 openml==0.10.2,sklearn==0.21.3 Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the binomial or multinomial deviance loss function. Binary classification is a special case where only a single regression tree is induced. 2019-11-22T02:17:29 English sklearn==0.21.3 numpy>=1.6.1 scipy>=0.9 criterion string "friedman_mse" The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "mse" for mean squared error, and "mae" for the mean absolute error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases .. versionadded:: 0.18 init estimator or null An estimator object that is used to compute the initial predictions ``init`` has to provide `fit` and `predict_proba`. If 'zero', the initial raw predictions are set to zero. By default, a ``DummyEstimator`` predicting the classes priors is used learning_rate float 0.1 learning rate shrinks the contribution of each tree by `learning_rate` There is a trade-off between learning_rate and n_estimators loss "deviance" max_depth integer 3 maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables max_features int null The 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` Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias 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 max_leaf_nodes int or None null Grow trees with ``max_leaf_nodes`` in best-first fashion Best nodes are defined as relative reduction in impurity If None then unlimited number of leaf nodes min_impurity_decrease float 0.0 A 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:: 0.19 min_impurity_split float null Threshold 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`` instead min_samples_leaf int 1 The 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 fractions min_samples_split int 2 The 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 fractions min_weight_fraction_leaf float 0.0 The 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 n_estimators int 100 The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance n_iter_no_change int null ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. The split is stratified .. versionadded:: 0.20 presort bool or "auto" Whether to presort the data to speed up the finding of best splits in fitting. Auto mode by default will use presorting on dense data and default to normal sorting on sparse data. Setting presort to true on sparse data will raise an error .. versionadded:: 0.17 *presort* parameter random_state int 0 If 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` subsample float 1.0 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators` Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias tol float 0.0001 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops .. versionadded:: 0.20 validation_fraction float 0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if ``n_iter_no_change`` is set to an integer .. versionadded:: 0.20 verbose int 0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree warm_start bool false When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. See :term:`the Glossary <warm_start>` openml-python python scikit-learn sklearn sklearn_0.21.3