Summary of Probabilistic Scores Of Classifiers, Calibration Is Not Enough, by Agathe Fernandes Machado et al.
Probabilistic Scores of Classifiers, Calibration is not Enough
by Agathe Fernandes Machado, Arthur Charpentier, Emmanuel Flachaire, Ewen Gallic, François Hu
First submitted to arxiv on: 6 Aug 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 study explores the importance of accurate probabilistic predictions in real-world applications such as predicting payment defaults or assessing medical risks. The researchers highlight the limitations of traditional calibration metrics when score heterogeneity deviates from the underlying data probability distribution, leading to misaligned predicted probabilities and actual outcomes. To address this issue, they propose optimizing tree-based models like Random Forest and XGBoost by minimizing the Kullback-Leibler (KL) divergence between predicted and true distributions. The analysis demonstrates that this approach yields superior alignment without significant performance loss across 10 UCI datasets and simulations. The study’s findings emphasize the need to prioritize KL divergence optimization over traditional calibration metrics, as minimizing these metrics can lead to suboptimal results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure predictions are accurate for things like predicting whether someone will pay their bills on time or if they’ll get sick. It shows that current methods don’t work well when there’s a lot of variation in the data. The researchers suggest using special models called Random Forest and XGBoost to make better predictions. They tested these models on lots of different datasets and showed that they work well without sacrificing accuracy. |
Keywords
» Artificial intelligence » Alignment » Optimization » Probability » Random forest » Xgboost
