Data Science Data Science Supervised Learning Models Questions and Answers 2

Question 1

Which supervised learning algorithm constructs a series of if-then rules by recursively partitioning the feature space?

Accepted Answer

Decision Tree

Answer

K-Means Clustering

Answer

Principal Component Analysis

Answer

DBSCAN

Question 2

What is the primary purpose of regularization in supervised learning models such as Ridge and Lasso regression?

Accepted Answer

To prevent overfitting by penalizing large coefficients

Answer

To increase the number of features in the model

Answer

To convert categorical variables into numerical ones

Answer

To speed up the training process

Question 3

In a Random Forest classifier, what technique is used to reduce variance and improve generalization?

Accepted Answer

Bagging with random feature subsets

Answer

Gradient boosting with sequential trees

Answer

Forward feature selection

Answer

Backward elimination of correlated features

Question 4

Which metric is most appropriate for evaluating a supervised classification model when the dataset has a severe class imbalance?

Accepted Answer

Area Under the Precision-Recall Curve (AUPRC)

Answer

Accuracy

Answer

Mean Squared Error

Answer

R-squared

Question 5

What does the kernel trick enable Support Vector Machines to do?

Accepted Answer

Map data into a higher-dimensional space to find nonlinear decision boundaries

Answer

Reduce the number of support vectors needed

Answer

Automatically select the best regularization parameter

Answer

Perform unsupervised clustering on the data

Question 6

In gradient boosting, what does each successive tree in the ensemble attempt to model?

Accepted Answer

The residual errors of the previous ensemble

Answer

A random subset of the original target values

Answer

The inverse of the previous tree's predictions

Answer

The feature importances from the prior iteration