`sklearn.ensemble`.HistGradientBoostingRegressor¶

class sklearn.ensemble.HistGradientBoostingRegressor(loss='squared_error', *, quantile=None, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=255, categorical_features=None, monotonic_cst=None, warm_start=False, early_stopping='auto', scoring='loss', validation_fraction=0.1, n_iter_no_change=10, tol=1e-07, verbose=0, random_state=None)[source]¶

Histogram-based Gradient Boosting Regression Tree.

This estimator is much faster than GradientBoostingRegressor 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.

See also

GradientBoostingRegressor: Exact gradient boosting method that does not scale as good on datasets with a large number of samples.
sklearn.tree.DecisionTreeRegressor: A decision tree regressor.
RandomForestRegressor: A meta-estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the statistical performance and control over-fitting.
AdaBoostRegressor: A meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases.

Examples

>>> from sklearn.ensemble import HistGradientBoostingRegressor
>>> from sklearn.datasets import load_diabetes
>>> X, y = load_diabetes(return_X_y=True)
>>> est = HistGradientBoostingRegressor().fit(X, y)
>>> est.score(X, y)
0.92...

Methods

`fit`(X, y[, sample_weight])	Fit the gradient boosting model.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict values for X.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`staged_predict`(X)	Predict regression target for each iteration.

fit(X, y, sample_weight=None)[source]¶

Fit the gradient boosting model.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples.
yarray-like of shape (n_samples,): Target values.
sample_weightarray-like of shape (n_samples,) default=None: Weights of training data.

New in version 0.23.

Returns:

selfobject: Fitted estimator.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

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

Returns:

paramsdict: Parameter names mapped to their values.

property n_iter_¶: Number of iterations of the boosting process.

predict(X)[source]¶

Predict values for X.

Parameters:

Xarray-like, shape (n_samples, n_features): The input samples.

Returns:

yndarray, shape (n_samples,): The predicted values.

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

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: \(R^2\) of self.predict(X) wrt. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)[source]¶

Set the parameters of this estimator.

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

Parameters: