Precision, Recall and Accuracy for Classification models

The accuracy of a classification model is judged by whether the predicted class is the same as the actual class. Let us assume the levels of a class as either positive or negative, then -

  • For all predicted positives, they can be either true (right) or false (wrong).
  • For all predicted negatives, they can be either true (right) or false (wrong).

Precision is a ratio defined as (True Positives)/(Predicted Positives). Ideally 1.

Recall is a ratio defined as (True Positives)/(Actual Positives). It is also called Sensitivity or True Positive Rate (TPR). Ideally 1.

Specificity is a ratio defined as (True Negatives)/(Actual Negatives). It is also called Selectivity. Ideally 1.

Accuracy is a ratio defined as (True Positives + True Negatives)/(Total Predictions). Ideally 1.

Python Implementation: sklearn.metrics -> classification_report

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