Data Science with Python Certification Data Science with Python Model Evaluation and Validation 4

Question 1

Which of the following correctly describes 'precision' in a binary classification context?

Accepted Answer

TP / (TP + FP)

Answer

Precision measures the fraction of positive predictions that are actually correct: TP / (TP + FP).

Question 2

In sklearn, what does cross_val_score return by default?

Accepted Answer

An array of scores for each fold

Answer

cross_val_score returns a numpy array containing one score per fold, allowing you to compute statistics like mean and std.

Question 3

What is the primary advantage of nested cross-validation over standard cross-validation for model selection?

Accepted Answer

It provides an unbiased estimate of the selected model's true generalization error

Answer

Nested CV uses an outer loop for performance estimation and an inner loop for hyperparameter tuning, preventing selection bias from leaking into the performance estimate.

Question 4

A model achieves 99% accuracy on a fraud detection dataset where 99% of transactions are legitimate. What does this reveal?

Accepted Answer

Accuracy is misleading; the model likely predicts 'legitimate' for everything

Answer

Predicting the majority class for every sample yields 99% accuracy on this dataset, meaning the model detects zero fraudulent transactions.

Question 5

What does the 'recall' (sensitivity) metric specifically measure in binary classification?

Accepted Answer

Fraction of actual positives that were correctly identified

Answer

Recall = TP / (TP + FN), measuring how well the model finds all actual positive cases in the dataset.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Model Evaluation and Validation 4