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New estimators for Bayes-optimal BER and AUC in binary classification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods for estimating the Bayes-optimal balanced error rate (BER) and area under the ROC curve (AUC) in binary classification tasks. These estimators are designed to work even when soft labels are corrupted and the class prior is unknown, utilizing isotonic regression to approximate clean soft labels. The proposed framework also includes an evaluation procedure, extending the FeeBee system, to assess the performance of these estimators on real-world datasets without needing to know the true optimal values. AI

IMPACT Provides advanced tools for evaluating model performance, particularly in imbalanced datasets, aiding in more robust machine learning development.

RANK_REASON Academic paper detailing new estimation and evaluation methods for classification metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New estimators for Bayes-optimal BER and AUC in binary classification

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Academic paper detailing new estimation and evaluation methods for classification metrics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Ryota Ushio, Takashi Ishida, Masashi Sugiyama ·

    Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators

    arXiv:2609.02304v1 Announce Type: cross Abstract: A fundamental quantity in machine learning is the optimal performance achievable by any model on a given task. Estimating this quantity allows us to distinguish the irreducible part of the error from a deficiency of the model, tel…

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