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

K-Nearest Neighbors

Answer

Naive Bayes

Answer

Random Forest

Question 2

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

Accepted Answer

The residual errors of the previous ensemble

Answer

The bias of the first tree only

Answer

The variance of a single decision tree

Answer

The feature importance rankings

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

The data follows a uniform distribution

Answer

All features contribute equally to the prediction

Answer

The decision boundary is always linear

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

Lasso regression (L1)

Answer

Elastic Net

Answer

Stepwise regression

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

It reduces the number of support vectors needed

Answer

It eliminates the need for regularization parameters

Answer

It automatically selects the best features

Question 6

In a decision tree, what does the Gini impurity measure of 0 indicate about a node?

Accepted Answer

All samples in the node belong to a single class

Answer

The node has equal representation of all classes

Answer

The node should be pruned

Answer

The split at that node is invalid