Perpetual

Contents:

  • Installation
  • Quickstart
  • API Reference
  • Causal ML
  • Calibration and Uncertainty Quantification
  • Drift Detection
  • Continual Learning
  • Explainability
  • Model IO & Export
  • Tutorials
    • Handling Categorical Data
    • Scikit-Learn Interface: Classification, Regression & Ranking
    • Performance Benchmarking
    • Mastering Regression Calibration: From Theoretical Basics to Advanced Methods
    • Classification Calibration: Prediction Sets with Perpetual
    • Custom Objective Tutorial
    • Uplift Modeling with the Criteo Uplift Dataset
    • 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
    • Advanced Drift Detection in Perpetual
  • Architecture
  • Parameters Tuning
  • Frequently Asked Questions
Perpetual
  • Tutorials
  • View page source

Tutorials

Tutorials

  • Handling Categorical Data
  • Scikit-Learn Interface: Classification, Regression & Ranking
  • Performance Benchmarking
  • Mastering Regression Calibration: From Theoretical Basics to Advanced Methods
  • Classification Calibration: Prediction Sets with Perpetual
  • Custom Objective Tutorial
  • Uplift Modeling with the Criteo Uplift Dataset
  • 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
  • Advanced Drift Detection in Perpetual
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