Machine Learning Model Evaluation 2

Question 1

Which metric is most appropriate when the cost of false negatives is much higher than false positives, such as in cancer screening?

Accepted Answer

Recall

Answer

Recall (sensitivity) measures the proportion of actual positives correctly identified, minimizing missed cases (false negatives).

Question 2

What does a ROC curve plot on its axes?

Accepted Answer

True Positive Rate vs False Positive Rate

Answer

A ROC curve plots True Positive Rate (sensitivity) on the y-axis against False Positive Rate (1-specificity) on the x-axis.

Question 3

If a model achieves AUC-ROC of 0.5, what does this indicate?

Accepted Answer

Performance equivalent to random guessing

Answer

An AUC of 0.5 means the model has no discriminative ability and performs no better than random chance.

Question 4

In k-fold cross-validation with k=5, what percentage of data is used for training in each fold?

Accepted Answer

80%

Answer

With k=5, the data is split into 5 folds; each iteration uses 4 folds (80%) for training and 1 fold (20%) for validation.

Question 5

What is the purpose of a confusion matrix?

Accepted Answer

To display counts of true/false positives and negatives

Answer

A confusion matrix tabulates TP, TN, FP, and FN counts to give a detailed breakdown of classification performance.

Machine Learning Practice Test