A financial company has deployed an ML model to predict customer churn. The model has been running in production for 1 week. The company wants to evaluate how accurately the model predicts churn…

The correct answer is C. F1 score. The F1 score combines precision (how many predicted churners actually churned) and recall (how many actual churners were correctly identified) into a single metric, making it ideal for measuring your model’s accuracy on the binary churn - prediction task.

Submitted by kavita_s· Mar 30, 2026Machine Learning Implementation and Operations

Question

A financial company has deployed an ML model to predict customer churn. The model has been running in production for 1 week. The company wants to evaluate how accurately the model predicts churn compared to actual customer behavior. Which metric meets these requirements?

Options

ARoot mean squared error (RMSE)
BReturn on investment (ROI)
CF1 score
DBilingual Evaluation Understudy (BLEU) score

How the community answered

(36 responses)

A
11% (4)
B
3% (1)
C
81% (29)
D
6% (2)

Explanation

The F1 score combines precision (how many predicted churners actually churned) and recall (how many actual churners were correctly identified) into a single metric, making it ideal for measuring your model’s accuracy on the binary churn - prediction task.

Topics

#ML model evaluation#Classification metrics#F1 score

Community Discussion

No community discussion yet for this question.

Full AIF-C01 Practice