IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

To determine the precision of the system, it is essential to understand what precision represents in the context of classification tasks. Precision is defined as the ratio of true positive results to the total number of predicted positive results. In this case, we want to calculate the precision for the detection of red fish.

Given that there is one red fish detected, we can assume this is classified as a true positive. The system has also detected five blue fish, which are not red fish. Since precision only considers the positive predictions (in this case, the red fish), the equation for precision can be defined as:

Precision = True Positives / (True Positives + False Positives)

With one red fish detected (true positive), and no false positives (since all detected blue fish do not contribute to false positives as they are not predicted as red), it simplifies to:

Precision = 1 / (1 + 0) = 1 or 100%.

Thus, when considering the context, the precision of the system is accurately represented as 100%, as the only positive class detected perfectly matches the true condition.