Welcome to the documentation of skpro – a domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions, in supervised contexts.

Contents

• Introduction
  • Features
  • A motivating example
• Installation
  • Bleeding edge
• User guide
  • Overview
  • Available prediction strategies
  • Advanced topics
  • Help and support
• Baseline strategies
  • DensityBaseline
• Composite parametric prediction
  • Overview
  • Estimators
  • Example
• Vendor integrations
  • PyMC3
  • Integrate other models
• Meta-modelling
  • Hyperparamter optimization
  • Pipelines
  • Ensemble methods
• Workflow automation
  • Model-view-controller structure
  • Result aggregation and comparison
  • Code example
• Custom strategies
  • Developing custom models
  • Integrating vendor models
• API Reference
  • skpro package
• Changelog
  • Version 1.0b
• Authors
• Contributing & Citation
  • Contributing guide
  • Cite skpro
• License

Indices and tables

• Index
• Module Index
• Search Page