Data Science Data Science Model Performance and Evaluation Questions and Answers 2

Question 1

Which metric is most appropriate for evaluating a classification model when the dataset has a severe class imbalance?

Accepted Answer

Precision-Recall AUC

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

Question 2

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

Accepted Answer

The model is overfitting the training data

Answer

The model is underfitting the data

Answer

The model has reached optimal performance

Answer

The model needs more features

Question 3

In k-fold cross-validation with k=10, how many times is each data point used in the test set?

Accepted Answer

1 time

Answer

10 times

Answer

5 times

Answer

It depends on the dataset size

Question 4

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

Accepted Answer

To assess whether predicted probabilities match actual outcome frequencies

Answer

To measure feature importance

Answer

To determine the optimal number of clusters

Answer

To visualize training loss over epochs

Question 5

Which evaluation approach is most suitable when you need to assess a time-series forecasting model?

Accepted Answer

Walk-forward validation

Answer

Random k-fold cross-validation

Answer

Stratified sampling

Answer

Leave-one-out cross-validation

Question 6

What does a Matthews Correlation Coefficient (MCC) of 0 indicate about a binary classifier?

Accepted Answer

Performance no better than random guessing

Answer

Perfect classification

Answer

The model always predicts the positive class

Answer

The model has zero false positives