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sklearn.ensemble.gradient_boosting.GradientBoostingClassifier

sklearn.ensemble.gradient_boosting.GradientBoostingClassifier

Visibility: public Uploaded 13-08-2021 by Sergey Redyuk sklearn==0.18 numpy>=1.6.1 scipy>=0.9 8 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18
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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.

Parameters

criterionThe 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:: 0.18default: "friedman_mse"
initAn estimator object that is used to compute the initial predictions. ``init`` has to provide ``fit`` and ``predict`` If None it uses ``loss.init_estimator``default: null
learning_ratelearning rate shrinks the contribution of each tree by `learning_rate` There is a trade-off between learning_rate and n_estimatorsdefault: 0.1
lossdefault: "deviance"
max_depthmaximum 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 variablesdefault: 3
max_featuresThe 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 percentage 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`` featuresdefault: null
max_leaf_nodesGrow 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 nodesdefault: null
min_impurity_splitThreshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf .. versionadded:: 0.18default: 1e-07
min_samples_leafThe minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node .. versionchanged:: 0.18 Added float values for percentagesdefault: 1
min_samples_splitThe 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 percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split .. versionchanged:: 0.18 Added float values for percentagesdefault: 2
min_weight_fraction_leafThe minimum weighted fraction of the input samples required to be at a leaf nodedefault: 0.0
n_estimatorsThe number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performancedefault: 100
presortWhether 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.default: "auto"
random_stateIf 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`default: null
subsampleThe 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 biasdefault: 1.0
verboseEnable 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 treedefault: 0
warm_startWhen set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solutiondefault: false

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