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  • Overview
  • The trade-off
  • Combining Precision and Recall

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  1. ML & Data Science
  2. Frequent Questions

Precision vs Recall

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Last updated 3 years ago

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Overview

  • expresses the percentage of results correctly classified.

  • means the percentage of your results that are relevant.

  • refers to the percentage of total relevant results correctly classified by your algorithm.

The trade-off

We can see when our precision is 1.0 (no FP), our recall remains very low because we still have many FN. If we go to the other extreme and classify all inputs as negatives, we will have a recall of 1.0 but our precision will be very low and we’ll detain many innocent individuals. In other words, as we increase precision we decrease recall and vice-versa.

Depending on the situation, we may maximize either precision (i.e. spam detection) or recall (i.e. disease detection).

The is useful for quickly calculating precision and recall given the predicted labels from a model. The other main visualization technique for showing the performance of a classification model is the .

Combining Precision and Recall

If we want to create a balanced classification model with the optimal balance of recall and precision, then we try to maximize the F1 score.

We use the harmonic mean instead of a simple average because it punishes extreme values. A classifier with a precision of 1.0 and a recall of 0.0 has a simple average of 0.5 but an F1 score of 0.

In cases where we want to find an optimal blend of precision and recall we can combine the two metrics using what is called the . It is the harmonic mean of precision and recall taking both metrics into account.

Metrics
How does a ROC curve work?
Precision vs Recall (Shurti Saxena)
Identification of Similar and Complementary Subparts in B-Rep Mechanical Models
Beyond Accuracy: Precision and Recall (Will Koehrsen)
Receiver Operating Characteristic (ROC) curve
F1 score
Accuracy
Precision
Recall
confusion matrix