Data Science FREE Data Science Feature Engineering and Selection Questions and Answers 2

Question 1

Which technique creates new features by combining two or more existing categorical variables into a single feature?

Accepted Answer

Feature crossing

Answer

Feature crossing combines categorical variables to capture interaction effects between them.

Question 2

What is the primary purpose of using Variance Inflation Factor (VIF) in feature selection?

Accepted Answer

To detect multicollinearity among predictor variables

Answer

VIF quantifies how much a feature's variance is inflated due to correlation with other predictors.

Question 3

When performing target encoding on a categorical feature, what problem can arise if no regularization is applied?

Accepted Answer

Target leakage leading to overfitting

Answer

Without regularization, target encoding can leak target information into features, causing severe overfitting.

Question 4

Which feature selection method evaluates subsets of features by actually training a model and measuring performance?

Accepted Answer

Wrapper method

Answer

Wrapper methods use a predictive model to score feature subsets and select the best-performing combination.

Question 5

What is the main advantage of using mutual information over Pearson correlation for feature selection?

Accepted Answer

It captures non-linear relationships between variables

Answer

Mutual information measures any statistical dependency between variables, not just linear relationships.

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