To evaluate machine learning models, we need some metrics. There are many ways of measuring classification performance. Accuracy, F1 score, Precision, Recall are a few of the commonly used metrics to evaluate machine learning models. They are calculated based on four parameters: false positive, false negative, true positive, and true negative. A confusion matrix is a table that is often used to describe the performance of a classification model, based on the four discussed parameters.
Machine learning model evaluation metrics
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