Oct 27, 2003 · This article provides a characterization of bias for evaluation metrics in classification (e.g., Information Gain, Gini, χ2, etc.).
Aug 10, 2022 · This paper provides a characterization of bias for evaluation metrics in classification (e.g., Information Gain, Gini, 2 , etc.).
We give a practical value to our measure by observing the distance between the bias of two evaluation metrics and its correlation with differences in predictive ...
A Quantification of Distance Bias Between Evaluation Metrics In Classification. Authors: Ricardo Vilalta. Ricardo Vilalta. View Profile. , Daniel Oblinger.
The paper demonstrates that the graphical depiction of machineLearning metrics by means of ROC isometrics gives many useful insights into the ...
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Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev. Hosted as a part of SLEBOK on ...
Oct 2, 2024 · Evaluation Metrics in Classification: A Quantification of Distance‐Bias. R. Vilalta, Daniel Oblinger. 2003, International Conference on ...
Sep 27, 2023 · Our guide, packed with examples and a step-by-step process, shows you how to tackle data sampling bias and master feature engineering for fairness.
Oct 22, 2024 · Distance metrics deal with finding the proximity or distance between data points and determining if they can be clustered together.
Mar 13, 2024 · Our aim here is to introduce the most common metrics for binary and multi-class classification, regression, image segmentation, and object detection.