5CSM1_ML LAB_B2

Which of the following assumptions is NOT required for linear regression?

• • Linearity of relationships

• • Homoscedasticity

• • Normality of predictor variables

• • Independence of residuals

In multiple linear regression, what is the primary purpose of including multiple predictor variables?

• • To reduce the variance of the response variable

• • To account for more complex relationships between predictors and the response

• • To increase the bias of the model

• To simplify the regression model

What is the purpose of the least squares method in linear regression?

• • To maximize the variance of the predictor variables

• • To minimize the sum of the squared residuals

• • To calculate the mean of the dependent variable

• • To estimate the error rate of the model

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

• Multivariate Logistic Regression
  
  

• Logistic Regression
  
  

• Linear Regression
  
  

• Multivariate Linear Regression

How many variables are required to represent a linear regression model?

• 3

• 2

• 1

• 4

In PCA, what does the term "principal component" refer to?

• • A variable that represents the maximum variance in the data

• • A new feature created as a linear combination of the original features

• • The original features that are not modified

• • The process of scaling the data

What does the eigen value associated with a principal component represent in PCA?

• • The proportion of variance explained by the principal component

• • The mean of the data along that principal component

• • The variance of the residuals after projection

• The number of original features used to compute the principal component

Why is it often important to standardize the data before applying PCA?

• • To remove all outliers from the dataset

• • To ensure that each feature contributes equally to the principal components

• • To increase the dimensionality of the data

• • To normalize the range of the target variable

Which of the following is a characteristic of the principal components in PCA?

• • They are uncorrelated with each other

• • They are correlated with each other

• • They are the original features of the dataset

• • They are not linear combinations of the original features

What is one major advantage of reducing the dimensionality of a dataset?

• • It increases the risk of over fitting

• • It enhances the interpretability of the model

• • It always improves the accuracy of the model

• • It removes the need for data preprocessing