IBM Data Science Practice Test

Precision is a metric used to evaluate the performance of a classification model, specifically in scenarios where the positive class is of particular interest. It measures the accuracy of the positive predictions made by the model.

To calculate precision, the formula used is:

Precision = True Positives / (True Positives + False Positives)

In this case, the model has 80 true positives and 20 false positives. Plugging these values into the formula gives:

Precision = 80 / (80 + 20)

Precision = 80 / 100

Precision = 0.80 or 80%

This result indicates that 80% of the instances predicted as positive are indeed positive, demonstrating the model's reliability in identifying true positive cases. A higher precision value is desirable as it shows that when the model predicts a positive class, it is rarely wrong.