skopt.learning.GradientBoostingQuantileRegressor¶

class skopt.learning.GradientBoostingQuantileRegressor(quantiles=[0.16, 0.5, 0.84], base_estimator=None, n_jobs=1, random_state=None)[source][source]¶

Predict several quantiles with one estimator.

This is a wrapper around GradientBoostingRegressor’s quantile regression that allows you to predict several quantiles in one go.

Parameters

quantilesarray-like

Quantiles to predict. By default the 16, 50 and 84% quantiles are predicted.

base_estimatorGradientBoostingRegressor instance or None (default)

Quantile regressor used to make predictions. Only instances of GradientBoostingRegressor are supported. Use this to change the hyper-parameters of the estimator.

n_jobsint, default=1

The number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores.

random_stateint, RandomState instance, or None (default)

Set random state to something other than None for reproducible results.

Methods

fit(self, X, y)	Fit one regressor for each quantile.
get_params(self[, deep])	Get parameters for this estimator.
predict(self, X[, return_std, return_quantiles])	Predict.
score(self, X, y[, sample_weight])	Return the coefficient of determination R^2 of the prediction.
set_params(self, \*\*params)	Set the parameters of this estimator.

__init__(self, quantiles=[0.16, 0.5, 0.84], base_estimator=None, n_jobs=1, random_state=None)[source][source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y)[source][source]¶

Fit one regressor for each quantile.

Parameters

Xarray-like, shape=(n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

yarray-like, shape=(n_samples,)

Target values (real numbers in regression)

get_params(self, 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

paramsmapping of string to any

Parameter names mapped to their values.

predict(self, X, return_std=False, return_quantiles=False)[source][source]¶

Predict.

Predict X at every quantile if return_std is set to False. If return_std is set to True, then return the mean and the predicted standard deviation, which is approximated as the (0.84th quantile - 0.16th quantile) divided by 2.0

Parameters

Xarray-like, shape=(n_samples, n_features)

where n_samples is the number of samples and n_features is the number of features.

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

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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, 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 R2 score used when calling score on a regressor will use multioutput='uniform_average' from version 0.23 to keep consistent with r2_score(). This will influence the score method of all the multioutput regressors (except for MultiOutputRegressor). To specify the default value manually and avoid the warning, please either call r2_score() directly or make a custom scorer with make_scorer() (the built-in scorer 'r2' uses multioutput='uniform_average').

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.

Parameters

**paramsdict

Estimator parameters.

Returns

selfobject

Estimator instance.