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
- Quickstart
- API Reference
- Tutorials
- Handling Categorical Data
- Quick Start with Toy Datasets
- Scikit-Learn Interface: Classification, Regression & Ranking
- Performance Benchmarking
- Custom Objective Tutorial
- Uplift Modeling and Causal Inference
- Instrumental Variables (Boosted IV)
- Double Machine Learning: Estimating the Gender Wage Gap
- Policy Learning: Optimal Treatment Assignment
- Risk, Compliance, and Interpretability
- Heterogeneous Treatment Effects with Meta-Learners
- Fairness-Aware Credit Scoring
- Fairness-Aware Classification with FairClassifier
- Customer Retention: Uplift Modeling for Churn Prevention
- Causal ML
- Architecture
- Parameters Tuning
- Frequently Asked Questions
API Reference
See the API Reference for the detailed API reference.