Time series regression

The skpro.regression module contains algorithms and composition tools for probabilistic supervised regression, i.e., tabular regression estimator with a probabilistic prediction mode.

All regressors in skpro can be listed using the skpro.registry.all_objects utility, using object_types="regressor_proba", optionally filtered by tags. Valid tags can be listed using skpro.registry.all_tags.

Composition

Pipeline(steps)

Pipeline for probabilistic supervised regression.

Model selection and tuning

GridSearchCV(estimator, cv, param_grid[, ...])	Perform grid-search cross-validation to find optimal model parameters.
RandomizedSearchCV(estimator, cv, ...[, ...])	Perform randomized-search cross-validation to find optimal model parameters.

evaluate(estimator, cv, X, y[, scoring, ...])

Evaluate estimator using re-sample folds.

Reduction - adding predict_proba

This section lists reduction algorithms that take one or multiple sklearn estimators and adda probabilistic prediction mode.

BootstrapRegressor(estimator[, ...])

Bootstrap ensemble of a tabular regressor.

ResidualDouble(estimator[, estimator_resid, ...])

Residual double regressor.

Base

BaseProbaRegressor([index, columns])

Base class for probabilistic supervised regressors.