deephyper.skopt.learning.GradientBoostingQuantileRegressor

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 or None) – Quantile regressor used to make predictions. Only instances of GradientBoostingRegressor are supported. Use this to change the hyper-parameters of the estimator. Defaults to None.

  • n_jobs (int) – Number of jobs to run in parallel for fit. If -1, then the number of jobs is set to the number of cores. Defaults to 1.

  • random_state (int | RandomState | None) – Controls the randomness of the estimator. Set this to something other than None for reproducible results. Defaults to None.

Methods

fit

Fit one regressor for each quantile.

predict

Predict.

set_params

__call__(*args: Any, **kwargs: Any) → Any#

Call self as a function.

fit(X, y)[source]#

Fit one regressor for each quantile.

Args: 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.

yarray-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

Args: X : array-like, shape=(n_samples, n_features)

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

Contents