Advanced Infrastructure Penetration Testing

Machine learning model evaluation metrics

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.