Machine learning Quiz 3

Multivariate linear regression belongs to which category?

• a) Neither supervised nor unsupervised learning
  

• b) Both supervised and unsupervised learning
  

• c) Supervised learning
  

• d) Unsupervised learning

Which of the following is true about logistic regression?

• a) It is a type of unsupervised learning

• b) It is used for regression problems

• c) It is a type of classification algorithm

• d) It is not suitable for binary classification

What is the purpose of regularization in machine learning models?

• a) To increase bias and reduce variance

• b) To reduce bias and increase variance

• c) To prevent overfitting

• d) To make the model more complex

Which evaluation metric is commonly used for regression problems?

• a) F1 Score

• b) Mean Absolute Error

• c) Precision

• d) Recall

The learner is trying to predict housing prices based on the size of each house and number of bedrooms. What type of regression is this?

• a) Multivariate Logistic Regression
  

• b) Logistic Regression
  

• c) Linear Regression
  

• d) Multivariate Linear Regression

What is the minimum number of variables required to represent a linear regression model?

• a) 3
  

• b) 2
  

• c) 1
  

• d) 4

What is the main difference between linear regression and logistic regression?

• a) Linear regression is used for classification problems, while logistic regression is used for regression problems

• b) Linear regression is a type of unsupervised learning, while logistic regression is a type of supervised learning

• c) Linear regression predicts continuous values, while logistic regression predicts probabilities

• d) Linear regression is a non-parametric model, while logistic regression is a parametric model

When should regularization be applied in machine learning models?

• a) Only for linear regression models

• b) When the model is underfitting the training data

• c) When the model is overfitting the training data

• d) Regularization is not necessary in machine learning models

Which of the following is a common assumption in linear regression?

• a) The residuals are normally distributed

• b) The independent variables are not related to each other

• c) The relationship between the independent and dependent variables is linear

• d) The model does not require any training data

Which of the following is a common assumption in logistic regression?

• a) The residuals are normally distributed

• b) The independent variables are not related to each other

• c) The relationship between the independent and dependent variables is linear

• d) The model does not require any training data