Summary of Pathological Regularization Regimes in Classification Tasks, by Maximilian Wiesmann et al.
Pathological Regularization Regimes in Classification Tasks
by Maximilian Wiesmann, Paul Larsen
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 This paper explores the phenomenon of “trend reversal” in binary classification tasks, where a trained model’s performance changes unexpectedly when moving from one dataset to another. The researchers show that this can occur when using certain types of regularization during training, which they term the “pathological regularization regime.” They provide specific conditions for when such regimes exist and demonstrate these findings through numerical examples of logistic regression models. The study aims to give data scientists a tool to avoid making hyperparameter choices that lead to trend reversal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how machine learning models can sometimes behave unexpectedly when we switch from one dataset to another. The researchers want to understand why this happens and how we can prevent it. They found that certain ways of training the model, called “pathological regularization,” can cause this problem. They also show examples of this happening with a type of machine learning model called logistic regression. Overall, this study aims to help data scientists make better choices when working with their models. |
Keywords
* Artificial intelligence * Classification * Hyperparameter * Logistic regression * Machine learning * Regularization
