Match each ML model evaluation concept to its definition.
Drag a concept onto its matching description — or click a concept then click the description.
Model performs well on training data but poorly on unseen data
Model fails to capture underlying patterns in data
Error from wrong assumptions in the learning algorithm
Error from sensitivity to small fluctuations in training data
Balance between underfitting and overfitting
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Precision: The proportion of true positive predictions among all positive predictions
Precision, Recall, F1 Score, and ROC AUC are key evaluation metrics. Common confusions include swapping Accuracy, Specificity, and Precision definitions.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Precision: The proportion of true positive predictions among all positive predictions
Why this is correct
Precision measures the accuracy of positive predictions.
Recall: The proportion of true positive predictions among all actual positives
Why this is correct
Recall measures the ability to find all positive instances.
F1 Score: The harmonic mean of precision and recall
Why this is correct
F1 Score balances precision and recall.
ROC AUC: The proportion of correct predictions among total predictions
Why it's wrong here
Incorrect — this describes Accuracy, not ROC AUC.
Accuracy: The ability to correctly identify negative cases
Why it's wrong here
Incorrect — this describes Specificity, not Accuracy.
Specificity: The proportion of true positive predictions among all positive predictions
Why it's wrong here
Incorrect — this describes Precision, not Specificity.
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About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.