sklearn.ensemble.HistGradientBoostingClassifier

class sklearn.ensemble.HistGradientBoostingClassifier(loss=’auto’, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=256, scoring=None, validation_fraction=0.1, n_iter_no_change=None, tol=1e-07, verbose=0, random_state=None)[source]

Histogram-based Gradient Boosting Classification Tree.

This estimator is much faster than GradientBoostingClassifier for big datasets (n_samples >= 10 000). The input data X is pre-binned into integer-valued bins, which considerably reduces the number of splitting points to consider, and allows the algorithm to leverage integer-based data structures. For small sample sizes, GradientBoostingClassifier might be preferred since binning may lead to split points that are too approximate in this setting.

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:

>>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_hist_gradient_boosting  # noqa
>>> # now you can import normally from ensemble
>>> from sklearn.ensemble import HistGradientBoostingClassifier
Parameters:
loss : {‘auto’, ‘binary_crossentropy’, ‘categorical_crossentropy’}, optional (default=’auto’)

The loss function to use in the boosting process. ‘binary_crossentropy’ (also known as logistic loss) is used for binary classification and generalizes to ‘categorical_crossentropy’ for multiclass classification. ‘auto’ will automatically choose either loss depending on the nature of the problem.

learning_rate : float, optional (default=1)

The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Use 1 for no shrinkage.

max_iter : int, optional (default=100)

The 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 built.

max_leaf_nodes : int or None, optional (default=31)

The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit.

max_depth : int or None, optional (default=None)

The 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 default.

min_samples_leaf : int, optional (default=20)

The 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 built.

l2_regularization : float, optional (default=0)

The L2 regularization parameter. Use 0 for no regularization.

max_bins : int, optional (default=256)

The maximum number of bins to use. Before training, each feature of the input array X is binned into at most max_bins bins, which allows for a much faster training stage. Features with a small number of unique values may use less than max_bins bins. Must be no larger than 256.

scoring : str or callable or None, optional (default=None)

Scoring parameter to use for early stopping. It can be a single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions). 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 None.

validation_fraction : int or float or None, optional (default=0.1)

Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data.

n_iter_no_change : int or None, optional (default=None)

Used 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 done.

tol : float or None, optional (default=1e-7)

The 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 process.

random_state : int, np.random.RandomStateInstance or None, optional (default=None)

Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. See random_state.

Attributes:
n_iter_ : int

The number of estimators as selected by early stopping (if n_iter_no_change is not None). Otherwise it corresponds to max_iter.

n_trees_per_iteration_ : int

The number of tree that are built at each iteration. This is equal to 1 for binary classification, and to n_classes for multiclass classification.

train_score_ : ndarray, shape (max_iter + 1,)

The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the scoring parameter. If scoring is not ‘loss’, scores are computed on a subset of at most 10 000 samples. Empty if no early stopping.

validation_score_ : ndarray, shape (max_iter + 1,)

The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the scoring parameter. Empty if no early stopping or if validation_fraction is None.

Examples

>>> # To use this experimental feature, we need to explicitly ask for it:
>>> from sklearn.experimental import enable_hist_gradient_boosting  # noqa
>>> from sklearn.ensemble import HistGradientBoostingRegressor
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> clf = HistGradientBoostingClassifier().fit(X, y)
>>> clf.score(X, y)
1.0

Methods

decision_function(self, X) Compute the decision function of X.
fit(self, X, y) Fit the gradient boosting model.
get_params(self[, deep]) Get parameters for this estimator.
predict(self, X) Predict classes for X.
predict_proba(self, X) Predict class probabilities for X.
score(self, X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels.
set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, loss=’auto’, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=256, scoring=None, validation_fraction=0.1, n_iter_no_change=None, tol=1e-07, verbose=0, random_state=None)[source]
decision_function(self, X)[source]

Compute the decision function of X.

Parameters:
X : array-like, shape (n_samples, n_features)

The input samples.

Returns:
decision : ndarray, shape (n_samples,) or (n_samples, n_trees_per_iteration)

The raw predicted values (i.e. the sum of the trees leaves) for each sample. n_trees_per_iteration is equal to the number of classes in multiclass classification.

fit(self, X, y)[source]

Fit the gradient boosting model.

Parameters:
X : array-like, shape=(n_samples, n_features)

The input samples.

y : array-like, shape=(n_samples,)

Target values.

Returns:
self : object
get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
params : mapping of string to any

Parameter names mapped to their values.

predict(self, X)[source]

Predict classes for X.

Parameters:
X : array-like, shape (n_samples, n_features)

The input samples.

Returns:
y : ndarray, shape (n_samples,)

The predicted classes.

predict_proba(self, X)[source]

Predict class probabilities for X.

Parameters:
X : array-like, shape (n_samples, n_features)

The input samples.

Returns:
p : ndarray, shape (n_samples, n_classes)

The class probabilities of the input samples.

score(self, X, y, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:
X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:
score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:
self