Calculating accuracy metrics: precision

The Precision score is an 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 Positives, or $$\frac{\text{# of True Positives}}{\text{# of True Positives} + \text{# of False Positives}}.$$

we define True Positives as the number of employees who actually left, and were classified correctly as leaving
we define False Positives as the number of employees who actually stayed, but were wrongly classified as leaving

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

In this exercise, we will calculate the precision score (using the sklearn function precision_score) for our initial classification model.

The variables features_test and target_test are available in your workspace.

This exercise is part of the course

HR Analytics: Predicting Employee Churn in Python

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Exercise instructions

Import the function precision_score from the module sklearn.metrics.
Use the initial model to predict churn (based on features of the test set).
Calculate the precision score by comparing target_test with the test set predictions.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the function to calculate precision score
from sklearn.____ import ____

# Predict whether employees will churn using the test set
prediction = model.____(features_test)

# Calculate precision score by comparing target_test with the prediction
____(target_test, ____)

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