Data Science FREE Data Science Model Evaluation and Validation Questions and Answers 2

Question 1

Which metric is most appropriate for evaluating a classification model on a highly imbalanced dataset?

Accepted Answer

Precision-Recall AUC

Answer

Precision-Recall AUC is preferred for imbalanced datasets because accuracy can be misleadingly high when the majority class dominates predictions.

Question 2

What does a high variance and low bias in a model typically indicate?

Accepted Answer

Overfitting

Answer

High variance with low bias means the model fits training data very well but fails to generalize, which is the hallmark of overfitting.

Question 3

In stratified k-fold cross-validation, what is preserved across each fold?

Accepted Answer

The proportion of each class label

Answer

Stratified k-fold ensures each fold maintains the same class distribution as the original dataset, which is critical for representative evaluation.

Question 4

What is the primary purpose of a calibration curve (reliability diagram)?

Accepted Answer

To assess whether predicted probabilities match actual outcomes

Answer

A calibration curve plots predicted probabilities against observed frequencies to show whether a model's confidence scores are trustworthy.

Question 5

When using bootstrapping for model evaluation, what is the typical approach?

Accepted Answer

Sampling with replacement to create multiple training sets

Answer

Bootstrapping creates multiple resampled datasets by sampling with replacement from the original data, allowing robust estimation of model performance statistics.

Data Science Practice Test

Data Science Practice Test

Data Science FREE Data Science Model Evaluation and Validation Questions and Answers 2