skopt.learning.GradientBoostingQuantileRegressor

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

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

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

fit(X, y)

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(deep=True)

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.

predict(X, return_std=False, return_quantiles=False)

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(X, y, sample_weight=None)

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)

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.