Detailed Answer in Step-by-Step Solution:

Objective: Choose the better model for cancer prediction based on metrics.

Understand Metrics:

Accuracy: Overall correct predictions.

Recall: True positives / (True positives + False negatives)—crucial for cancer (minimizing misses).

Context: Cancer prediction prioritizes recall—false negatives (missed cancers) are critical.

Evaluate Models:

Model 1: 80% accuracy, 70% recall—Misses more cancers.

Model 2: 75% accuracy, 85% recall—Misses fewer cancers.

Evaluate Options:

A: High recall—True, but lacks context.

B: High accuracy—Misses recall’s importance.

C: Recall’s impact—Correct for cancer use case—best.

D: Lesser recall impact—Incorrect for this priority.

Reasoning: C emphasizes recall’s critical role—aligns with medical needs.

Conclusion: C is correct.

OCI documentation advises: “For critical predictions like cancer detection, prioritize recall (e.g., Model 2 at 85%) over accuracy (Model 1 at 80%) to minimize false negatives, as missing cases has severe consequences (C).” A is partial, B overlooks context, D reverses priority—only C fits OCI’s ML evaluation guidance for this scenario.

Oracle Cloud Infrastructure Data Science Documentation, "Evaluating Classification Models".