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.
Este exercício faz parte do curso
HR Analytics: Predicting Employee Churn in Python
Instruções do exercício
- Import the function
precision_scorefrom the modulesklearn.metrics. - Use the initial model to predict churn (based on features of the test set).
- Calculate the precision score by comparing
target_testwith the test set predictions.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# 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, ____)