Quantile machine learning models for python

This module provides quantile machine learning models for python, in a plug-and-play fashion in the sklearn environment. This means that practically the only dependency is sklearn and all its functionality is applicable to the here provided models without code changes.

Two tutorials explain the development of Random Forest Quantile regression. The same results and considerations are valid for K-nearest neighbours quantile regression and Extra Trees quantile regression.

Documentation

Getting Started

• Installation
  • Required dependencies
  • Instructions

Methods

• Quantile Regression Forest
• Quantile KNN

Tutorials

• Example usage
• Prediction Intervals for Quantile Regression Forests
  • Fitting non-linear quantile and least squares regressors
  • Analysis of the error metrics
  • Calibration of the confidence interval
  • Tuning the hyper-parameters of the quantile regressors

API Reference

• What’s new
  • v0.8.0 (13.4.2023)
• API reference
  • Models

License

sklearn_quantile is published under a BSD 3 clause license.

Indices and tables

• Index

• Module Index

• Search Page