Summary of Labels in Extremes: How Well Calibrated Are Extreme Multi-label Classifiers?, by Nasib Ullah and Erik Schultheis and Jinbin Zhang and Rohit Babbar
Labels in Extremes: How Well Calibrated are Extreme Multi-label Classifiers?
by Nasib Ullah, Erik Schultheis, Jinbin Zhang, Rohit Babbar
First submitted to arxiv on: 6 Nov 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 In this paper, researchers tackle extreme multilabel classification (XMLC) problems that involve evaluating millions of possible labels. They identify two key tasks: assessing each label’s worth and selecting the most relevant ones. The standard evaluation procedure focuses on relative scores, but in practical applications, accurate probability estimates are crucial. To address this, they introduce calibrated probabilities and propose a new measure, calibration@k (e.g., ECE@k), to evaluate XMLC models. They demonstrate that different models exhibit varying reliability plots, but post-training calibration via isotonic regression enhances model performance without sacrificing accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves extreme multilabel classification problems by evaluating millions of possible labels. It’s like trying to find the best ads for your website or recommending products people will love. The challenge is in figuring out how likely each label is to be relevant. Right now, most models don’t provide accurate probabilities. This paper wants to change that by introducing a new way to measure model calibration and showing how it can improve performance. |
Keywords
» Artificial intelligence » Classification » Probability » Regression
