Data Science Feature Engineering and Selection 3

Question 1

Which feature selection method evaluates subsets of features using the actual model's performance?

Accepted Answer

Wrapper methods

Answer

Filter methods

Answer

One-hot encoding

Answer

Normalization

Question 2

Filter methods select features based on:

Accepted Answer

Statistical measures independent of any model

Answer

The trained model's coefficients

Answer

Cross-validated model accuracy

Answer

Gradient updates during training

Question 3

L1 (Lasso) regularization performs feature selection by:

Accepted Answer

Shrinking some coefficients exactly to zero

Answer

Squaring all coefficients

Answer

Adding interaction terms

Answer

Scaling features to unit variance

Question 4

Two features have a correlation of 0.97. What is a common action in feature selection?

Accepted Answer

Drop one of the redundant features

Answer

Multiply them together

Answer

Keep both and double their weight

Answer

Convert both to categorical

Question 5

Which metric measures multicollinearity among features?

Accepted Answer

Variance Inflation Factor (VIF)

Answer

F1 score

Answer

ROC AUC

Answer

Silhouette score

Question 6

Recursive Feature Elimination (RFE) works by:

Accepted Answer

Iteratively removing the least important features and refitting

Answer

Adding random features each round

Answer

Scaling features recursively

Answer

Imputing missing values repeatedly

Question 7

A filter method using mutual information captures:

Accepted Answer

Both linear and non-linear dependency with the target

Answer

Only linear correlation

Answer

Only the feature's variance

Answer

The model's training time