.png){kind=icon}
返回
Towards optimal model evaluation: enhancing active testing with actively improved estimators
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-58633-3.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With rapid advancements in machine learning and statistical models, ensuring the reliability of these models through accurate evaluation has become imperative. Traditional evaluation methods often rely on fully labeled test data, a requirement that is becoming increasingly impractical due to the growing size of datasets. In this work, we address this issue by extending existing work on active testing (AT) methods which are designed to sequentially sample and label data for evaluating pre-trained models. We propose two novel estimators: the Actively Improved Levelled Unbiased Risk (AILUR) and the Actively Improved Inverse Probability Weighting (AIIPW) estimators which are derived from nonparametric smoothing estimation. In addition, a model recalibration process is designed for the AIIPW estimator to optimize the sampling probability within the AT framework. We evaluate the proposed estimators on four real-world datasets and demonstrate that they consistently outperform existing AT methods. Our study also shows that the proposed methods are robust to changes in subsample sizes, and effective at reducing labeling costs.
Keyword:
VALIDATION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.0W
被引数:
83.5W
机构
引用论文
Dispersion and Beating of Bacterial Cellulose and their Influence on Paper Properties.png)
BioResourcesOAAI
BioResourcesIF0
OAAI分享收藏分享收藏分享收藏Reduced calcium elevation in hippocampal CA1 neurons of ischemia-tolerant gerbilsNeuroReport
NeuroReportIF0
PREAI
分享收藏 没有更多内容
没有更多内容