Summary of Investigating Data Usage For Inductive Conformal Predictors, by Yizirui Fang and Anthony Bellotti
Investigating Data Usage for Inductive Conformal Predictors
by Yizirui Fang, Anthony Bellotti
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 development process of Inductive Conformal Predictors (ICPs), which generate prediction sets instead of point predictions, allowing for reliable machine learning. ICPs are useful for various applications and have gained popularity. The study examines the most efficient way to divide limited or expensive development data into training, calibration, and test sets. Experiments are conducted to investigate the possibility of overlapping examples between training and calibration sets. The findings will be valuable for academics and practitioners using ICPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new kind of machine learning tool called Inductive Conformal Predictors (ICPs). They’re useful because they can predict a range of outcomes instead of just one, making them more reliable. The researchers looked at how to develop these tools efficiently, using limited data. They also explored whether it’s okay to share some examples between the different parts of the development process. The results will be helpful for people who want to use ICPs in their work. |
Keywords
* Artificial intelligence * Machine learning
