Machine Learning

What is the main difference between classification and regression?

• Classification predicts continuous values, while regression predicts categories

• Classification predicts categories, while regression predicts continuous values

• Both classification and regression predict continuous values

• None of the above

Which of the following is an example of a classification problem?

• Predicting house prices

• Predicting customer churn (Yes/No)

• • Predicting a student’s exam score

• Predicting the temperature of a city

What is a Decision Tree in Machine Learning?

• A type of tree that grows based on input data

• A flowchart-like model used for decision-making

• A clustering technique

• None of the above

What is the stopping criteria for growing a decision tree?

• All data is classified perfectly

• Maximum depth is reached

• A node has too few samples

• All of the above

How does a Random Forest model improve accuracy?

• By combining multiple Decision Trees

• By using only the most important features

• By using a single deep Decision Tree

• By making random guesses

What technique does Random Forest use to create multiple trees?

• Gradient Boosting

• Bagging (Bootstrap Aggregating)

• Clustering

• Feature Selection

What is the purpose of the kernel trick in SVM?

• To make SVM work faster

• To transform data into a higher dimension for better separation

• To remove outliers

• To reduce overfitting

Which of the following is NOT a commonly used kernel in SVM?

• Linear

• Polynomial

• Decision Tree

• • Radial Basis Function (RBF)

A bank wants to determine whether a customer is likely to default on a loan. Which machine learning technique is most suitable?

• Regression

• Classification

• Clustering

• Reinforcement Learning

A real estate company wants to predict the price of houses based on size, location, and number of rooms. Which algorithm is most appropriate?

• Decision Tree

• Logistic Regression

• Linear Regression

• SVM