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sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier

sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier

Visibility: public Uploaded 16-11-2019 by Nicolas Hug sklearn==0.23.dev0 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.dev0
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Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples >= 10 000). This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. This implementation is inspired by `LightGBM `_. .. note:: This estimator is still experimental for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import ``enable_hist_gradient_boosting``:: >>> # explicit...

Parameters

l2_regularizationThe L2 regularization parameter. Use 0 for no regularizationdefault: 0.0
learning_rateThe learning rate, also known as *shrinkage*. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkagedefault: 0.1
lossdefault: "auto"
max_binsThe maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255default: 255
max_depthThe maximum depth of each tree. The depth of a tree is the number of nodes to go from the root to the deepest leaf. Must be strictly greater than 1. Depth isn't constrained by defaultdefault: null
max_iterThe maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are builtdefault: 100
max_leaf_nodesThe maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limitdefault: 31
min_samples_leafThe minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be builtdefault: 20
n_iter_no_changeUsed to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. If None or 0, no early-stopping is donedefault: null
random_statePseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. See :term:`random_state`.default: 38391
scoringScoring parameter to use for early stopping. It can be a single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`). If None, the estimator's default scorer is used. If ``scoring='loss'``, early stopping is checked w.r.t the loss value. Only used if ``n_iter_no_change`` is not Nonedefault: null
tolThe absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score verbose: int, optional (default=0) The verbosity level. If not zero, print some information about the fitting processdefault: 1e-07
validation_fractionProportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training datadefault: 0.1
verbosedefault: 0
warm_startWhen set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble. For results to be valid, the estimator should be re-trained on the same data only See :term:`the Glossary `default: false

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