Evaluation Metrics - Machine Learning Quiz

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

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

Precision

Recall

Accuracy

F1-Score

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

4. What is the formula for Recall?

TP / (TP + FP)

TP / (TP + FN)

FP / (TP + FP)

TP + TN / Total

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

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Quiz 5: Evaluation Metrics