/scoring

Scoring rules vs scoring functions playground

Primary LanguagePythonMIT LicenseMIT

Learning Scoring Rules

Scoring rules vs scoring functions playground

Description

Welcome to a repository dedicated to learning about scoring rules and functions. This repository contains materials and examples designed to help students and professionals alike understand the fundamental concepts and practical applications of scoring rules in various fields like statistics, decision theory, and machine learning.

Contents

simple_example.py - This script contains basic examples demonstrating the use of a scoring function (Mean Squared Error) and a scoring rule (Brier Score). The examples are designed to provide a clear and straightforward introduction to these concepts.

Learning Objectives

  • Understand the difference between scoring rules and scoring functions.
  • Learn how to apply scoring rules like the Brier Score in evaluating probabilistic predictions.
  • Develop skills in implementing these concepts in Python, utilizing libraries like sklearn.

How to Use This Repository

  • Begin by exploring the simple_example.py file to get an initial understanding of scoring functions and rules.
  • Read through the comments in the script for explanations and context about each example.
  • Experiment by modifying the script and observing the changes in output, enhancing your understanding of how these scoring methods work.
  • Check back for updates, as more examples and educational materials will be added regularly.

Contributions

Contributions and suggestions to improve the learning experience are welcome. If you have examples, explanations, or additional materials that could benefit others in understanding scoring rules and functions, please feel free to submit a pull request or open an issue.

License

Please note that all materials in this repository are provided under MIT License. By using or contributing to this repository, you agree to abide by its terms.