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- W4313400850 abstract "One of the most important topics in machine learning is how to evaluate the models, which means measuring how accurately they predict the expected outcome. In addition to accuracy, there are a number of other metrics for evaluating classifier performance. Model evaluation is introduced in a prediction framework that is implemented using automated machine learning. The performance metrics are calculated for each classification model generated for our analysis. Unlabeled data gathered using a 360-degree evaluation form goes through a clustering process before being analyzed by classification. The most performant classifier is identified through model evaluation, and a detailed metrics analysis is performed." @default.
- W4313400850 created "2023-01-06" @default.
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- W4313400850 date "2023-01-01" @default.
- W4313400850 modified "2023-09-27" @default.
- W4313400850 title "Metrics for Evaluating Classification Algorithms" @default.
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- W4313400850 doi "https://doi.org/10.1007/978-981-19-6755-9_24" @default.
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