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 severalquantiles
in one go. Parameters
 quantilesarraylike
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 hyperparameters 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 R^2 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)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y)[source][source]¶ Fit one regressor for each quantile.
 Parameters
 Xarraylike, shape=(n_samples, n_features)
Training vectors, where
n_samples
is the number of samples andn_features
is the number of features. yarraylike, shape=(n_samples,)
Target values (real numbers in regression)

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
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(X, return_std=False, return_quantiles=False)[source][source]¶ Predict.
Predict
X
at every quantile ifreturn_std
is set to False. Ifreturn_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
 Xarraylike, shape=(n_samples, n_features)
where
n_samples
is the number of samples andn_features
is the number of features.

score
(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
 Xarraylike 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.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike 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 usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

set_params
(**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.