Which evaluation metric is best for a model predicting customer churn?
Practice Questions
Q1
Which evaluation metric is best for a model predicting customer churn?
Mean Squared Error
F1 Score
R-squared
Log Loss
Questions & Step-by-Step Solutions
Which evaluation metric is best for a model predicting customer churn?
Step 1: Understand what customer churn means. Customer churn is when customers stop using a company's service or product.
Step 2: Know that when predicting churn, we want to identify customers who are likely to leave.
Step 3: Learn about evaluation metrics. These are ways to measure how well our prediction model is performing.
Step 4: Recognize two important metrics: Precision and Recall. Precision tells us how many of the predicted churners actually churned, while Recall tells us how many of the actual churners we correctly identified.
Step 5: Understand that in customer churn prediction, we want to minimize false negatives (missing customers who will churn) and also want to ensure that we are not falsely predicting too many customers will churn.
Step 6: The F1 Score combines Precision and Recall into one number, which helps us balance the two. A higher F1 Score means better performance in identifying churners.
Step 7: Conclude that the F1 Score is a good choice for evaluating a model predicting customer churn because it helps us find the right balance between Precision and Recall.
