Jun 12, 2016 · In this paper, a new performance measure for classification algorithms based on Receiver Operator Characteristic (ROC) curves is proposed with ...
The weights of true positive rates are proportional to classification difficulty index, while those of false positive rates are inversely proportional to ...
This paper aims to propose a new measure of classifier quality assessment, called the preference-driven measure, abbreviated p-d, regardless of the number ...
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How do you assess the performance of classification methods?
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Machine learning and deep learning classification models are data-driven, and the model and the data jointly determine their classification performance.
Nov 10, 2022 · In this paper, we propose a new separability measure--the rate of separability (RS), which is based on the data coding rate.
Missing: degree difficulty.
Nov 9, 2017 · Classification accuracy is the number of correct predictions divided by the total number of predictions. Accuracy can be misleading. For example ...
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Oct 29, 2024 · The purpose of a difficulty measure is to provide a means to estimate a model's performance. To that end, we employ the Pearson correlation ...
Machine learning and deep learning classification models are data-driven, and the model and the data jointly determine their classification performance.
May 10, 2024 · The ROC curve has been proven that is an excellent measure of classification performance, as it conveys more information than many ...
Oct 22, 2024 · Simply put, a classification metric is a number that measures the performance of your machine learning model in classification tasks.