CompTIA Data+ (DA0-002)Data AnalysisMedium
A data analyst is using a statistical model to predict the likelihood of customer churn. The model outputs a probability score for each customer. To evaluate the model's performance, the analyst needs to set a threshold above which a customer is classified as 'likely to churn'. They want to find a threshold that balances the rate of correctly identified churners with the rate of incorrectly identified non-churners. Which metric helps in evaluating this trade-off?
- ARecall
- BPrecision
- CAccuracy
- DF1-Score
Show answer & explanationAnswer & explanation
Correct answer: D. F1-Score
The F1-Score is the harmonic mean of precision and recall, providing a single metric that balances both. It is particularly useful when dealing with imbalanced classes, as it considers both false positives and false negatives.
Why the other options are wrong
- A. Recall measures the proportion of true positive predictions among all actual positives (minimizing false negatives).
- B. Precision measures the proportion of true positive predictions among all positive predictions (minimizing false positives).
- C. Accuracy measures overall correct predictions but can be misleading with imbalanced classes.
F1-Score
The harmonic mean of precision and recall, used as a single metric to evaluate binary classification models, especially with imbalanced datasets.
- Balances false positives (precision) and false negatives (recall).
- Ranges from 0 to 1, with 1 being perfect.
- Useful when both types of errors are costly.
Memory trick: F1 balances the 'F'ine line of errors.