Summary of Data-driven Estimation Of the False Positive Rate Of the Bayes Binary Classifier Via Soft Labels, by Minoh Jeong et al.
Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels
by Minoh Jeong, Martina Cardone, Alex Dytso
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); 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 The proposed paper introduces an innovative approach for estimating the optimal performance in binary classification problems. The authors develop an estimator for the false positive rate (FPR) of the Bayes classifier using soft labels, which have gained popularity due to their properties. The method is thoroughly examined for its consistency, unbiasedness, convergence rate, and variance. Additionally, the paper explores noisy labels, including binary labels, by leveraging a denoising technique and the Nadaraya-Watson estimator. This research has significant implications for data-driven methods in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how to measure if a machine learning model is doing its best job. The authors develop a way to estimate how many incorrect positive results (false positives) a Bayes classifier would make, given some data. They use “soft labels” which are like normal labels but with a value between 0 and 1. The method works well and can be used for other types of labels too. |
Keywords
* Artificial intelligence * Classification * Machine learning
