Summary of A Systematic Bias Of Machine Learning Regression Models and Its Correction: An Application to Imaging-based Brain Age Prediction, by Hwiyoung Lee et al.
A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction
by Hwiyoung Lee, Shuo Chen
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued outcomes tend to be negatively biased (underestimating actual values), while those for small-valued outcomes are positively biased (overestimating actual values). We refer to this linear central tendency warped bias as the “systematic bias of machine learning regression”. In this paper, we first demonstrate that this systematic prediction bias persists across various machine learning regression models, and then delve into its theoretical underpinnings. To address this issue, we propose a general constrained optimization approach designed to correct this bias and develop computationally efficient implementation algorithms. Simulation results indicate that our correction method effectively eliminates the bias from the predicted outcomes. We apply the proposed approach to the prediction of brain age using neuroimaging data. In comparison to competing machine learning regression models, our method effectively addresses the longstanding issue of “systematic bias of machine learning regression” in neuroimaging-based brain age calculation, yielding unbiased predictions of brain age. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can be bad at predicting values that are very different from what’s usual. They tend to get these weird values wrong, either underestimating or overestimating them. This is called the “systematic bias of machine learning regression”. In this paper, we show that many different kinds of machine learning models have this problem and then explain why it happens. We also propose a way to fix this issue using special math and algorithms. Our method works well in testing and can even be used to predict how old someone’s brain is based on their brain scans. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regression
