Perpetual Documentation

Perpetual is a self-generalizing gradient boosting machine that doesn’t need hyperparameter optimization. It is designed to be easy to use while providing state-of-the-art predictive performance.

Key Features

• Self-Generalizing: No need for complex grid searches or Bayesian optimization.

• Efficient: Built in Rust with a zero-copy Python interface.

• Versatile: Supports regression, classification, and ranking.

• Interpretable: Built-in support for SHAP-like contributions and partial dependence plots.

Contents:

• Installation
  • Requirements
  • Optional Dependencies
• Quickstart
  • Classification
  • Regression
  • Ranking
  • More Examples
• API Reference
  • PerpetualBooster
  • Sklearn Interface
• Tutorials
  • Handling Categorical Data
  • Quick Start with Toy Datasets
  • Performance Benchmarking
  • Custom Objective Tutorial
• Architecture
  • System Overview
  • Rust Core
  • Python Interface
  • PerpetualBooster Algorithm
• Parameters Tuning
  • The Budget Parameter
  • How it works
• Frequently Asked Questions
  • How do I choose the best budget?
  • Does Perpetual support categorical features?
  • Is Perpetual faster than XGBoost?

API Reference

See the API Reference for the detailed API reference.

Indices and tables

• Index

• Module Index

• Search Page