Jason2Brownlee/MachineLearningMischief
Machine Learning Mischief: Examples from the dark side of data science
This project offers clear demonstrations of how machine learning experiments can be intentionally manipulated to show misleadingly good results. It illustrates methods like 'cherry-picking' data or 'p-hacking' statistical tests to achieve a desired outcome. This is for anyone who reviews, manages, or is new to machine learning projects and needs to understand how to identify unreliable or fraudulent results.
Use this if you need to learn how to spot unethical manipulation of machine learning project results, or if you're a junior data scientist who needs to understand what practices to avoid.
Not ideal if you're looking for best practices or tools to improve the rigor and quality of your machine learning experiments, as this focuses on demonstrating manipulative techniques.
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Oct 24, 2025
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