Data Science FREE Data Science Supervised Learning Algorithms Questions and Answers 2

Question 1

Which supervised learning algorithm uses hyperplanes to separate data into classes and maximizes the margin between them?

Accepted Answer

Support Vector Machine

Answer

Support Vector Machines find the optimal hyperplane that maximizes the margin between classes in the feature space.

Question 2

In gradient boosting, what does each successive tree attempt to correct?

Accepted Answer

The residual errors of the previous ensemble

Answer

Each new tree in gradient boosting is trained on the residual errors left by the cumulative prediction of all prior trees.

Question 3

What is the primary assumption made by the Naive Bayes classifier that simplifies its computation?

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, which greatly simplifies probability calculations.

Question 4

Which regularization technique in linear regression adds the sum of squared coefficients as a penalty term to the loss function?

Accepted Answer

Ridge regression (L2)

Answer

Ridge regression applies L2 regularization by adding the sum of squared coefficients multiplied by a penalty parameter to the loss function.

Question 5

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

Accepted Answer

It enables classification of non-linearly separable data without explicitly mapping to higher dimensions

Answer

The kernel trick allows SVMs to operate in a high-dimensional feature space without explicitly computing the transformation, enabling non-linear decision boundaries.

Data Science Practice Test

Data Science Practice Test

Data Science FREE Data Science Supervised Learning Algorithms Questions and Answers 2