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How to Calculate Precision, Recall, and F-Measure for Imbalanced Classification

Classification accuracy is the total number of correct predictions
divided by the total number of predictions made for a dataset.


As a performance measure, accuracy is inappropriate for imbalanced classification problems.


The main reason is that the overwhelming number of examples from the
majority class (or classes) will overwhelm the number of examples in the
minority class, meaning that even unskillful models can achieve
accuracy scores of 90 percent, or 99 percent, depending on how severe
the class imbalance happens to be.


An alternative to using classification accuracy is to use precision and recall metrics.


In this tutorial, you will discover how to calculate and develop an
intuition for precision and recall for imbalanced classification.


After completing this tutorial, you will know:


  • Precision quantifies the number of positive class predictions that actually belong to the positive class.
  • Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.
  • F-Measure provides a single score that balances both the concerns of precision and recall in one number.

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