deephyper.skopt.learning.GradientBoostingQuantileRegressor#
- class deephyper.skopt.learning.GradientBoostingQuantileRegressor(*args: Any, **kwargs: Any)[source]#
Bases:
BaseEstimator
,RegressorMixin
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:
quantiles (array-like) – Quantiles to predict. By default the 16, 50 and 84% quantiles are predicted.
base_estimator (GradientBoostingRegressor 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_jobs (int, 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_state (int, RandomState instance, or None (default)) – Set random state to something other than None for reproducible results.
Methods
- __call__(*args: Any, **kwargs: Any) Any #
Call self as a function.
- fit(X, y)[source]#
Fit one regressor for each quantile.
- Parameters:
X (array-like, shape=(n_samples, n_features)) – Training vectors, where n_samples is the number of samples and n_features is the number of features.
y (array-like, shape=(n_samples,)) – Target values (real numbers in regression)
- predict(X, return_std=False, return_quantiles=False)[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:
X (array-like, shape=(n_samples, n_features)) – where n_samples is the number of samples and n_features is the number of features.