Data Science FREE Data Science Model Evaluation and Validation Questions and Answers 3

Question 1

Which technique helps detect data leakage during model validation?

Accepted Answer

Comparing training performance with cross-validation performance

Answer

Increasing the learning rate

Answer

Adding more regularization

Answer

Using a larger batch size

Question 2

What is the purpose of nested cross-validation?

Accepted Answer

To simultaneously perform hyperparameter tuning and unbiased performance estimation

Answer

To speed up model training

Answer

To increase the training dataset size

Answer

To reduce the number of features

Question 3

Which evaluation metric directly accounts for both the ranking quality and calibration of predicted probabilities?

Accepted Answer

Log Loss

Answer

F1 Score

Answer

Cohen's Kappa

Answer

Matthews Correlation Coefficient

Question 4

In time series model validation, why is standard k-fold cross-validation inappropriate?

Accepted Answer

It violates temporal ordering by using future data to predict the past

Answer

It requires too much computation

Answer

It only works with categorical targets

Answer

It cannot handle missing values

Question 5

What does McNemar's test evaluate when comparing two classifiers?

Accepted Answer

Whether they disagree on predictions in a statistically significant way

Answer

Whether their overall accuracies are identical

Answer

Whether they have the same number of parameters

Answer

Whether they use the same loss function

Question 6

What is the effect of using too few folds in k-fold cross-validation?

Accepted Answer

Higher bias in the performance estimate due to smaller training sets

Answer

Higher computational cost

Answer

Lower variance in the performance estimate

Answer

Increased risk of overfitting to the test set