MS-DS Master of Data science MS-DS Master of Data science Supervised Learning Algorithms Questions and Answers 2

Question 1

Which supervised learning algorithm uses the concept of maximum margin to separate classes?

Accepted Answer

Support Vector Machine

Answer

SVMs find the hyperplane that maximizes the margin between the closest data points of different classes.

Question 2

In gradient boosting, what does each subsequent tree attempt to predict?

Accepted Answer

The residual errors of the previous model

Answer

Each new tree in gradient boosting is trained on the residual errors left by the ensemble of all previous trees.

Question 3

What is the primary assumption of the Naive Bayes classifier?

Accepted Answer

Features are conditionally independent given the class label

Answer

Naive Bayes assumes that all features are conditionally independent of each other given the class label.

Question 4

Which regularization technique in linear regression can shrink coefficients exactly to zero, enabling feature selection?

Accepted Answer

L1 (Lasso)

Answer

L1 regularization (Lasso) penalizes the absolute value of coefficients, which can drive some coefficients to exactly zero.

Question 5

What problem does the kernel trick solve in Support Vector Machines?

Accepted Answer

It allows SVMs to find non-linear decision boundaries without explicitly mapping to higher dimensions

Answer

The kernel trick computes dot products in a high-dimensional space without explicitly transforming the data, enabling non-linear classification.

MS-DS Master of Data science Practice Test