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What is Evaluation Metrics in Machine Learning about?

This document discusses various evaluation metrics that can be used to evaluate machine learning models, including confusion matrices, accuracy, precision, recall, and F1 score. It provides definitions and formulas for calculating each of these metrics. Confusion matrices are used to assess true positives, false positives, true negatives, and false negatives. Accuracy measures overall correctness but does not provide detailed information. Precision is useful when false positives are costly, while recall is

Author

Denzil Segunto

Language

EN