FREE Data Science Feature Engineering and Selection Questions and Answers Flashcards
6 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 FREE Data Science Feature Engineering and Selection Questions and Answers flashcards as text
Which technique creates new features by combining two or more existing categorical variables into a single feature?
Answer: Feature crossing
Feature crossing combines categorical variables to capture interaction effects between them.
What is the primary purpose of using Variance Inflation Factor (VIF) in feature selection?
Answer: To detect multicollinearity among predictor variables
VIF quantifies how much a feature's variance is inflated due to correlation with other predictors.
When performing target encoding on a categorical feature, what problem can arise if no regularization is applied?
Answer: Target leakage leading to overfitting
Without regularization, target encoding can leak target information into features, causing severe overfitting.
Which feature selection method evaluates subsets of features by actually training a model and measuring performance?
Answer: Wrapper method
Wrapper methods use a predictive model to score feature subsets and select the best-performing combination.
What is the main advantage of using mutual information over Pearson correlation for feature selection?
Answer: It captures non-linear relationships between variables
Mutual information measures any statistical dependency between variables, not just linear relationships.
In time-series feature engineering, what does a lag feature represent?
Answer: The value of a variable at a previous time step
A lag feature shifts a variable's value by one or more time steps to capture temporal dependencies.