Regularization: Machine Learning Interview Prep 09

Regularization in machine learning is like adding constraints to prevent a model from becoming too complex. It’s like having guardrails on a road to keep a car from veering off course. This helps prevent overfitting, where the model memorizes the training data instead of learning patterns. Regularization encourages the model to generalize well to new, unseen data, making it more reliable in real-world situations.

Machine Learning Quiz 02: Ridge, Lasso

Let’s check your basic knowledge of Regularization: Ridge, Lasso, and Elastic Net. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

Question 1: An Ordinary Least Squares (OLS) is modified to minimize the absolute sum of the coefficients, this technique is called _____? (Select two)
(A) L1 regularization
(B) Lasso Regression
(C) L2 regularization
(D) Ridge Regression

Question 2: An Ordinary Least Squares (OLS) is modified to minimize the squared sum of the coefficients, this technique is called _____? (Select two)
(A) L2 regularization
(B) Ridge Regression
(C) L1 regularization
(D) Lasso Regression

Question 3: Which one is a regularization technique?
(A) Ridge Regression, which penalizes the sum of squared coefficients (L2 penalty)
(B) Lasso Regression, which penalizes the sum of absolute values of the coefficients (L1 penalty)
(C) Elastic Net, a convex combination of Ridge and Lasso
(D) All of the above

Question 4: Which one is a supervised Machine Learning technique?
(A) Ridge
(B) Lasso
(C) Elastic Net
(D) All of the above

Question 5: Which technique can overcome the drawbacks of Ridge and Lasso Regressor as well as a convex combination of these two algorithms?
(A) Support Vector Machine
(B) Linear Regression
(C) Elastic Net
(D) Logistic Regression

Question 6: Which one is correct about Elastic Net?
(A) Loss function = OLS loss function + 𝛼*Ridge Penalty + (1−𝛼)*LASSO Penalty; where 𝛼 ∈ [0,1]
(B) Loss function = 𝛼*Ridge Penalty + (1−𝛼)*LASSO Penalty; where 𝛼 ∈ [0,1]
(C) Loss function = OLS loss function + 𝛼 + (1−𝛼)*LASSO Penalty; where 𝛼 ∈ [0,1]
(D) None of these

Question 7: Which one is correct about Ridge regression?
(A) Loss function = OLS loss function + 𝛼*(square of the magnitude of the coefficients); where 0 < 𝛼 < ∞
(B) Loss function = 𝛼*(square of the magnitude of the coefficients); where 0 < 𝛼 < ∞
(C) Loss function = OLS loss function + 𝛼*(square root of the magnitude of the coefficients); where 0 < 𝛼 < ∞
(D) None of these

Question 8: Which one is correct about LASSO regression?
(A) Loss function = OLS loss function + 𝛼*(absolute sum of the coefficients); where 0 < 𝛼 < ∞
(B) Loss function = 𝛼*(absolute sum of the coefficients); where 0 < 𝛼 < ∞
(C) Loss function = OLS loss function + 𝛼*(sum of the coefficients); where 0 < 𝛼 < ∞
(D) None of these

Question 9: What are some solutions to prevent overfitting on sample data?
(A) Ridge
(B) Lasso
(C) Elastic Net
(D) All of the above

Question 10: If a linear regression model is underfitting, which regularization technique would you use?
(A) Ridge
(B) Lasso
(C) Elastic Net
(D) None of these

The solutions will be published in the next quiz Random Forest: Machine Learning Interview Prep 10.

Random Forest: Machine Learning Interview 10

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The solution of Linear Regression (Part 2): Machine Learning Interview Prep 08 - 1(D), 2(D), 3(A), 4(A), 5(B), 6(A, C), 7(D), 8(D), 9(A, B), 10(C)

Linear Regression (Part 2): Machine Learning Interview Prep 08

References:

[1] Regularization in R Tutorial: Ridge, Lasso and Elastic Net, https://www.datacamp.com/community/tutorials/tutorial-ridge-lasso-elastic-net

[2] What is elastic net regularization, and how does it solve the drawbacks of Ridge and Lasso? https://stats.stackexchange.com/questions/184029/what-is-elastic-net-regularization-and-how-does-it-solve-the-drawbacks-of-ridge#:~:text=L2%20regularization%20is%20suited,in%20the%20p%3En%20case.

[3] Ridge and Lasso Regression: L1 and L2 Regularization, https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b

[4] Linear Regression for Machine Learning, https://machinelearningmastery.com/linear-regression-for-machine-learning/

[5] Regularization Part 1: Ridge (L2) Regression, https://www.youtube.com/watch?v=Q81RR3yKn30&t=191s

[6] Regularization Part 2: Lasso (L1) Regression, https://www.youtube.com/watch?v=NGf0voTMlcs

[7] Ridge vs Lasso Regression, Visualized!!! https://www.youtube.com/watch?v=Xm2C_gTAl8c

[8] Regularization Part 3: Elastic Net Regression, https://www.youtube.com/watch?v=1dKRdX9bfIo

Photo: Balanced Rock Trail, Big Bend National Park, TX, USA Credit: Tasnim and Kawsar

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