Data Science Model Evaluation and Validation 2

Question 1

In a binary classifier, what does precision measure?

Accepted Answer

Of all predicted positives, how many were actually positive

Answer

Precision = TP / (TP + FP), the proportion of positive predictions that are correct.

Question 2

What does recall (sensitivity) measure?

Accepted Answer

Of all actual positives, how many were correctly identified

Answer

Recall = TP / (TP + FN), the proportion of actual positives correctly detected.

Question 3

Why is accuracy a poor metric for highly imbalanced datasets?

Accepted Answer

A model predicting the majority class always can score high while missing the minority class

Answer

With rare positives, always predicting the majority class yields high accuracy despite no useful detection.

Question 4

The F1 score is best described as the:

Accepted Answer

Harmonic mean of precision and recall

Answer

F1 = 2·(precision·recall)/(precision+recall), the harmonic mean balancing both.

Question 5

What does the ROC curve plot?

Accepted Answer

True positive rate against false positive rate across thresholds

Answer

The ROC curve plots TPR vs FPR as the classification threshold varies.

Data Science Practice Test