Summary of Adjusted Overfitting Regression, by Dylan Wilson
Adjusted Overfitting Regression
by Dylan Wilson
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper introduces “distance-based regression,” a novel approach that addresses overfitting and underfitting in machine learning models. By minimizing overfitting, distance-based regression can produce more accurate predictions. The author demonstrates the effectiveness of this method through a test and provides additional optimization techniques. The practical value of this technique is also showcased using a specific dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make better predictions by preventing a problem called “overfitting.” Overfitting happens when a model gets too good at fitting the noise in the data, rather than understanding the real patterns. This new method, called “distance-based regression,” helps models avoid overfitting and find more accurate results. The author shows that this approach works well by testing it and sharing ways to make it even better. They also apply this technique to a specific dataset to demonstrate its usefulness. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Overfitting » Regression » Underfitting
