Quantitatively, this paper proves that the Gini index normalized by that of the ideal classifier is equivalent to the area under the ROC curve above the chance�...
This paper proves that the normalized Area Under Upper ROC curve (above the random classifier diagonal) and the normalized Gini index are equiv- alent: Gini�...
A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment. July 2022.
This paper proves that the Gini index normalized by that of the ideal classifier is equivalent to the area under the ROC curve above the chance diagonal above�...
3, 2022. A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment. P Adeodato, S Melo�...
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A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment. P Adeodato, S Melo. 2022�...
A geometric proof of the equivalence between AUC_ROC and Gini index area metrics for binary classifier performance assessment � P. AdeodatoS�lvio B. Melo.
May 24, 2022 � AUC-ROC is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions.
In this article, we'll discuss three commonly used metrics for evaluating ML model quality: AUC/ROC curve, Kolmogorov–Smirnov score, and Gini Index.
Missing: geometric equivalence binary
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model