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Cyclic Boosting

  • Introduction
  • Install and Quickstart
  • Tutorial
  • Concepts
  • FAQ
    • cyclic_boosting
    • Contributing to Cyclic Boosting
  • GitHub
  • Introduction
  • Install and Quickstart
  • Tutorial
  • Concepts
  • FAQ
    • cyclic_boosting
    • Contributing to Cyclic Boosting
  • GitHub

Cyclic Boosting Documentation#

Cyclic Boosting is a Python library implementing the family of machine learning algorithms of the same name, which is described in Cyclic Boosting - an explainable supervised machine learning algorithm and Demand Forecasting of Individual Probability Density Functions with Machine Learning. It contains efficient, off-the-shelf, general-purpose supervised machine learning methods for both regression and classification tasks.

Contents:

  • Introduction
    • Regression
    • Classification
    • The Story behind Cyclic Boosting
  • Install and Quickstart
    • User Installation
    • Quickstart
    • Development and Tests
    • Linting and Formatting
  • Tutorial
    • Analysis Plots
    • Set Feature Properties
    • Set Features
    • Manual Binning
    • Feature Importances
    • Individual Explainability
    • Quantile Regression
  • Concepts
    • Binning
    • Training Procedure
    • Smoothing
    • Interaction Terms
    • Sample Weights
  • FAQ
    • Frequently asked questions
    • Troubleshooting
  • cyclic_boosting
    • cyclic_boosting package
  • Contributing to Cyclic Boosting
    • Reporting issues
    • Fixing issues
    • Adding features
    • Writing documentation
    • Submitting code changes
    • Code of Conduct

Indices and Tables#

  • Index

  • Module Index

  • Search Page

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