Classification Metrics Quiz

Which of the following best defines accuracy in a classification model?

• The proportion of correctly classified instances among all instances.

• The proportion of true positives out of the actual positives.

• The proportion of false positives among all negatives.

• The proportion of true negatives among all negatives.

Precision is a measure of:

• How often the model predicts correctly, regardless of class.

• How many true positive predictions are made relative to all positive predictions.

• How many true positive predictions are made relative to all actual positives.

• The proportion of negative samples correctly classified.

What does recall (or sensitivity) measure in a classification model?

• The ability of the model to predict true positives out of all actual positives.

• The proportion of false negatives among all negatives.

• The ability of the model to identify all negative cases.

• The number of true negatives relative to all predicted positives.

Why is the F1-Score useful in evaluating classification models?

• It combines precision and recall into a single metric, giving more weight to recall.

• It combines precision and recall into a single metric, giving equal weight to both.

• It provides a measure of accuracy weighted by the number of false negatives.

• It only focuses on the positive class predictions.

In a confusion matrix, what does the cell labeled as 'True Negative' represent?

• The number of positive instances correctly classified.

• The number of negative instances correctly classified.

• The number of positive instances misclassified as negative.

• The number of negative instances misclassified as positive.

This is a picture of a ? curve:

Specificity is defined as:

• The ability of the model to correctly identify negative instances.

• The ability of the model to correctly identify positive instances.

• The proportion of true positives among all positive predictions.

• The proportion of false negatives among all negative predictions.

What happens to the precision of a model if the number of false positives increases significantly?

• Precision increases because more samples are predicted as positive.

• Precision decreases because false positives are included in the denominator.

• Precision remains the same because it only depends on true positives.

• Precision is unaffected as it only considers recall.

Which of the following scenarios would result in high recall for a classification model?

• The model identifies most of the positive samples correctly, even if it misclassifies some negative samples.

• The model identifies most of the negative samples correctly, but misses some positive samples.

• The model avoids making any false positive predictions.

• The model has equal precision and specificity.

Why is the F1 score preferred over accuracy in imbalanced classification datasets?

• Because it considers both the true positives and true negatives equally.

• Because it balances the trade-off between precision and recall.

• Because it directly measures the false positive rate.

• Because it measures accuracy.