Compute Precision, Recall, and F1
Company: Intuit
Role: Data Scientist
Category: Statistics & Math
Difficulty: medium
Interview Round: Technical Screen
You are given a list of binary classification outputs, where each record contains:
- `actual` INT (`0` or `1`)
- `predict` INT (`0` or `1`)
- `conf` FLOAT, the model's confidence score for the positive class
Example record: `[actual=1, predict=0, conf=0.93]`.
Write Python or pseudocode to compute the following metrics for the positive class over the full dataset:
- `precision`
- `recall`
- `F1 score`
Also explain:
- how to derive `predict` from `conf` if the interviewer asks you to apply a threshold instead of using the provided predicted labels
- how to handle edge cases where a denominator becomes zero
Quick Answer: This question evaluates competency in computing and interpreting binary classification metrics—precision, recall, and F1—along with understanding thresholding of confidence scores and handling edge cases like zero denominators.