# A Gentle Introduction to the Fbeta-Measure for Machine Learning

Fbeta-measure is a configurable single-score metric for evaluating a binary classification model based on the predictions made for the positive class. The Fbeta-measure is calculated using precision and recall. Precision is a metric that calculates the percentage of correct predictions for the positive class. Recall calculates the percentage of correct predictions for the positive class out of all positive predictions that could be made. Maximizing precision will minimize the false-positive errors, whereas maximizing recall will minimize the false-negative errors. The […]

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