Data Science with Python Certification Data Science with Python Supervised Learning Models 5

Question 1

What is the key difference between Lasso (L1) and Ridge (L2) regularization in linear models?

Accepted Answer

Lasso can reduce coefficients to exactly zero, performing feature selection; Ridge shrinks but rarely zeros out

Answer

L1 (Lasso) regularization produces sparse solutions by driving some coefficients to exactly zero, effectively selecting features, while L2 shrinks all coefficients smoothly.

Question 2

In K-Nearest Neighbors classification, increasing k generally causes the model to:

Accepted Answer

Underfit with a smoother, less complex decision boundary

Answer

Larger k averages over more neighbors, smoothing the decision boundary and reducing variance at the cost of potentially higher bias.

Question 3

Which metric should be used for a multi-class classification problem with imbalanced classes?

Accepted Answer

Macro-averaged F1 Score

Answer

Macro-averaged F1 computes F1 per class independently and averages them, giving equal weight to each class regardless of support.

Question 4

What is the purpose of the learning_rate parameter in Gradient Boosting?

Accepted Answer

Shrinks the contribution of each tree to prevent overfitting

Answer

A smaller learning_rate means each tree contributes less, requiring more trees but often yielding better generalization.

Question 5

When would you use sklearn's PolynomialFeatures transformer before fitting a linear regression model?

Accepted Answer

When the relationship between features and target is nonlinear

Answer

PolynomialFeatures creates interaction and polynomial terms, allowing a linear model to capture nonlinear relationships in the data.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Supervised Learning Models 5