confidence-planner: Easy-to-Use Prediction Confidence Estimation and Sample Size Planning

TL;DR

This paper introduces 'confidence-planner', a user-friendly Python tool that estimates prediction confidence intervals and assists in sample size planning for machine learning applications, especially in sensitive fields like medicine and social sciences.

Contribution

The paper presents an accessible software package offering eight methods for confidence estimation and sample size planning, integrating seamlessly with existing data analysis workflows.

Findings

Provides eight procedures for confidence and sample size estimation.

Supports holdout, bootstrap, cross-validation, and progressive validation.

Facilitates statistical uncertainty estimation in socially impactful ML applications.

Abstract

Machine learning applications, especially in the fields of me\-di\-cine and social sciences, are slowly being subjected to increasing scrutiny. Similarly to sample size planning performed in clinical and social studies, lawmakers and funding agencies may expect statistical uncertainty estimations in machine learning applications that impact society. In this paper, we present an easy-to-use python package and web application for estimating prediction confidence intervals. The package offers eight different procedures to determine and justify the sample size and confidence of predictions from holdout, bootstrap, cross-validation, and progressive validation experiments. Since the package builds directly on established data analysis libraries, it seamlessly integrates into preprocessing and exploratory data analysis steps. Code related to this paper is available at: https://github.com/dabrze/confidence-planner.