Sources of Irreproducibility in Machine Learning: A Review

TL;DR

This paper reviews the causes of irreproducibility in machine learning, proposing a structured framework to understand how experiment design choices impact reproducibility and the validity of conclusions.

Contribution

It introduces a comprehensive framework linking experiment design factors to irreproducibility, aiding researchers in evaluating and improving ML study reproducibility.

Findings

Identified key factors affecting ML reproducibility

Organized factors within a scientific method-based framework

Demonstrated the framework with a model comparison study

Abstract

Background: Many published machine learning studies are irreproducible. Issues with methodology and not properly accounting for variation introduced by the algorithm themselves or their implementations are attributed as the main contributors to the irreproducibility.Problem: There exist no theoretical framework that relates experiment design choices to potential effects on the conclusions. Without such a framework, it is much harder for practitioners and researchers to evaluate experiment results and describe the limitations of experiments. The lack of such a framework also makes it harder for independent researchers to systematically attribute the causes of failed reproducibility experiments. Objective: The objective of this paper is to develop a framework that enable applied data science practitioners and researchers to understand which experiment design choices can lead to false findings and how and by this help in analyzing the conclusions of reproducibility experiments. Method: We have compiled an extensive list of factors reported in the literature that can lead to machine learning studies being irreproducible. These factors are organized and categorized in a reproducibility framework motivated by the stages of the scientific method. The factors are analyzed for how they can affect the conclusions drawn from experiments. A model comparison study is used as an example. Conclusion: We provide a framework that describes machine learning methodology from experimental design decisions to the conclusions inferred from them.