Machine Learning Regression 3

Question 1

What distinguishes Lasso regression from Ridge regression in terms of feature selection?

Accepted Answer

Lasso can shrink coefficients exactly to zero; Ridge cannot

Answer

Lasso's L1 penalty produces sparse solutions by setting some coefficients exactly to zero, effectively performing feature selection.

Question 2

In logistic regression, what function transforms the linear combination of features into a probability?

Accepted Answer

Sigmoid function

Answer

The sigmoid (logistic) function maps any real-valued number to a probability between 0 and 1.

Question 3

A residual plot shows a fan-shaped pattern as fitted values increase. This indicates:

Accepted Answer

Heteroscedasticity

Answer

A fan-shaped residual plot indicates heteroscedasticity — non-constant variance of residuals across fitted values.

Question 4

What is the purpose of the Variance Inflation Factor (VIF) in regression?

Accepted Answer

Detect multicollinearity among predictors

Answer

VIF quantifies how much the variance of a coefficient is inflated due to linear correlation with other predictors; VIF > 10 is concerning.

Question 5

In elastic net regression, which two penalties are combined?

Accepted Answer

L1 and L2

Answer

Elastic net combines L1 (Lasso) and L2 (Ridge) penalties, balancing sparsity and coefficient shrinkage.

Machine Learning Practice Test