Examination – Machine Learning

Question 1

What is Machine Learning?

Programming machines manually for every task

A technique that enables machines to learn from data

Hardware-based learning

A type of database management

Question 2

Which type of Machine Learning uses labeled data?

Unsupervised Learning

Reinforcement Learning

Supervised Learning

Deep Learning

Question 3

Which algorithm is commonly used for classification problems?

Linear Regression

K-Means

Decision Tree

Apriori

Question 4

What is the primary goal of regression?

Group data

Predict continuous values

Discover unknown patterns

Reduce dimensions

Question 5

Which evaluation metric is most suitable for classification problems?

Mean Squared Error (MSE)

Accuracy

R-Squared

Silhouette Score

Question 6

K-Means clustering is an example of:

Supervised Learning

Semi-supervised Learning

Unsupervised Learning

Reinforcement Learning

Question 7

Which technique is used to prevent overfitting?

Increasing model complexity

Removing training data

Regularization

Increasing learning rate

Question 8

What does “feature” mean in Machine Learning?

Output label

Input variable

Algorithm name

Dataset size

Question 9

Which algorithm is best suited for image recognition?

Linear Regression

Naive Bayes

Convolutional Neural Network (CNN)

K-Nearest Neighbor

Question 10

What is the purpose of a training dataset?

To test final model

To clean raw data

To train and learn patterns for the model

To deploy the model

Online Examination – Machine Learning