IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

To determine precision, it's essential to understand its definition in the context of classification problems. Precision is the ratio of true positive predictions to the total predicted positives. In simpler terms, it measures how many of the detected items truly belong to the positive class.

In this scenario, we have:

- Detected: 1 blue fish (true positive) and 3 red fish (false positive).

- Undetected: 4 blue fish (false negative) and 2 red fish (not applicable here for blue fish since we're focused on that class).

To calculate precision for blue fish:

- True positives (TP) = 1 (the blue fish that was correctly detected)

- False positives (FP) = 3 (the red fish that was incorrectly counted as blue fish)

The formula for precision is:

\[ \text{Precision} = \frac{TP}{TP + FP} \]

Substituting the values:

\[ \text{Precision} = \frac{1}{1 + 3} = \frac{1}{4} = 0.25 \]

It seems there was a misunderstanding with the assertion that the answer is A. The correct calculation leads us to a precision of 0.25, indicating that out