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

Principal Component Analysis

Answer

Normalization

Answer

Recursive feature elimination

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

To measure feature importance in tree models

Answer

To normalize skewed distributions

Answer

To reduce the number of target classes

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

Loss of all categorical information

Answer

Automatic removal of rare categories

Answer

Conversion of numeric features to categorical

Question 4

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

Accepted Answer

Wrapper method

Answer

Filter method

Answer

Embedded method

Answer

Transformation method

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

It runs faster on large datasets

Answer

It only works with continuous features

Answer

It requires no hyperparameter tuning

Question 6

In time-series feature engineering, what does a lag feature represent?

Accepted Answer

The value of a variable at a previous time step

Answer

The cumulative sum of all prior values

Answer

The difference between the maximum and minimum values

Answer

The seasonally adjusted trend component