Exercise

# Calculating accuracy metrics: recall

The Recall score is another important metric used to measure the accuracy of a classification algorithm. It is calculated as the** fraction of True Positives over the sum of True Positives and False Negatives**, or $$\frac{\text{# of True Positives}}{\text{# of True Positives} + \text{# of False Negatives}}.$$

If there are no False Negatives, the recall score is equal to 1. If there are no True Positives, the recall score is equal to 0.

In this exercise, you will calculate the precision score (using the sklearn function `recall_score`

) for your initial classification model.

The variables `features_test`

and `target_test`

are available in your workspace.

Instructions

**100 XP**

- Import the function to calculate the recall score.
- Use the initial model to predict churn (based on features of the test set).
- Calculate the recall score by comparing
`target_test`

with the predictions.