Probabilistic supervised regression

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

This learning task is sometimes also known as conditional distribution predictions, or conditional density estimation, if predictive distributions are continuous.

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.

MultipleQuantileRegressor(...[, ...])

Multiple quantile regressor.

MapieRegressor([estimator, method, cv, ...])

MAPIE probabilistic regressor, conformity score based prediction intervals.

CyclicBoosting([feature_groups, ...])

Cyclic boosting regressor.

Linear regression

ARDRegression([max_iter, tol, alpha_1, ...])	ARD regression, direct adapter to sklearn ARDRegression.
BayesianRidge([max_iter, tol, alpha_1, ...])	Bayesian ridge regression, direct adapter to sklearn BayesianRidge.
GLMRegressor([missing, start_params, ...])	Fits a generalized linear model with a gaussian link.
PoissonRegressor([alpha, fit_intercept, ...])	Poisson regression, direct adapter to sklearn PoissonRegressor.

Gaussian process and kernel regression

GaussianProcess([kernel, alpha, optimizer, ...])

Gaussian process (GP), direct interface to sklearn GaussianProcessRegressor.

Adapters to other interfaces

SklearnProbaReg(estimator[, inner_type])

Adapter to sklearn regressors with variance prediction interface.

Base classes

BaseProbaRegressor()

Base class for probabilistic supervised regressors.