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 recall score (using the sklearn function recall_score) for your initial classification model.
The variables features_test and target_test are available in your workspace.
Este ejercicio forma parte del curso
HR Analytics: Predicting Employee Churn in Python
Instrucciones del ejercicio
- 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_testwith the predictions.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Import the function to calculate recall score
from sklearn.____ import ____
# Use the initial model to predict churn
prediction = model.____(features_test)
# Calculate recall score by comparing target_test with the prediction
____(target_test, ____)