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Question 61 - DSA-C02 discussion

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Which metric is not used for evaluating classification models?

A.
Recall
Answers
A.
Recall
B.
Accuracy
Answers
B.
Accuracy
C.
Mean absolute error
Answers
C.
Mean absolute error
D.
Precision
Answers
D.
Precision
Suggested answer: C

Explanation:

The four commonly used metrics for evaluating classifier performance are:

1. Accuracy: The proportion of correct predictions out of the total predictions.

2. Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).

3. Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).

4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).

Root Mean Squared Error (RMSE)and Mean Absolute Error (MAE) are metrics used to evaluate a Regression Model. These metrics tell us how accurate our predictions are and, what is the amount of deviation from the actual values.

asked 23/09/2024
Ken Wilson
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