Evaluation Metrics

Test your understanding of Performance Metrics and their use in evaluating machine learning models.

FREE QUIZ

7 Questions

~14 Minutes

Beginner

Question 1

What does the Confusion Matrix help evaluate?

The relationship between features in a dataset The performance of a classification model The variance of a regression model The distribution of training data

Question 2

Which metric measures the proportion of correctly classified instances out of the total instances?

Precision Recall Accuracy F1-Score

Question 3

What does Precision quantify?

The proportion of actual positives correctly identified The proportion of true positive predictions out of all positive predictions The harmonic mean of Precision and Recall The number of false negatives in the model

Question 4

What is the formula for Recall?

TP / (TP + FP) TP / (TP + FN) FP / (TP + FP) TP + TN / Total

Question 5

What does the F1-Score represent?

The harmonic mean of Precision and Recall The sum of True Positives and True Negatives The difference between Accuracy and Precision The average of False Positives and False Negatives

Question 6

In a Confusion Matrix, what does a False Positive (FP) represent?

A negative case predicted as negative A positive case predicted as positive A negative case predicted as positive A positive case predicted as negative

Question 7

Why is balancing Precision and Recall important in model evaluation?

To maximize the number of true negatives To improve the variance of the model To ensure optimal performance in context-specific tasks To minimize training time

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