Summary of Predictability Analysis Of Regression Problems Via Conditional Entropy Estimations, by Yu-hsueh Fang and Chia-yen Lee
Predictability Analysis of Regression Problems via Conditional Entropy Estimations
by Yu-Hsueh Fang, Chia-Yen Lee
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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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 conditional entropy estimators to assess predictability in regression problems, addressing the limitation of traditional error metrics like mean squared error and coefficient of determination. The authors develop reliable estimators, including KNIFE-P and LMC-P, which provide a practical framework for predictability analysis. These estimators offer under- and over-estimation, enabling a more accurate evaluation of model performance. Extensive experiments on synthesized and real-world datasets demonstrate the robustness and utility of these estimators. The paper also extends the analysis to the coefficient of determination R^2, enhancing the interpretability of predictability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how well our machine learning models can predict things that happen next. Right now, we use special numbers like mean squared error to see if our models are good or not. But these numbers don’t tell us much about what’s actually happening. The authors of this paper create new tools called KNIFE-P and LMC-P that help us figure out how well our models can predict things. They test these tools on some fake data and real data, and they work really well! This is important because it helps us make better machine learning models. |
Keywords
» Artificial intelligence » Machine learning » Regression
