Linear Regression

What is the primary objective of linear regression?

• To classify data points into categories

• To find the relationship between two variables

• To reduce the dimensionality of data

• To cluster similar data points together

Linear regression is an algorithm for which of the following

• Classification

• Clustering

• Regression

• None of these

In linear regression, what does the term 'linear' refer to?

• The relationship between the predictors

• The relationship between the predictors and the response variable

• The distribution of the predictors

• The size of the dataset

Which of the following is a disadvantage of linear regression?

• It assumes a linear relationship between the independent and dependent variables.

• It is difficult to implement and understand.

• It always over fits the data

• It always under fit the data

What happens when the actual values are far away from the predicted line?

• Model will have good quality

• Model will lack quality in predictions

What does the line an algorithm generates indicate in linear regression?

• Set of Actual Values

• Set of predicted values

What happens to the error value when you try to predict a value way beyond the regression line?

• Error Increases

• Error Decreases

• Error Doesn't Change

• None of these

"Your actual values are closer to the regression line" - What does this indicate

• The model is fit Best for the dataset

• It is not a good fit

"Your actual values are far away from the regression line" - what does this indicate?

• Model is of good quality

• Model has less quality

What is the disadvantage of linear regression?

• It can only predict if the input output relation is linear

• It can only predict if the input output relation is Non-Linear

• It can only be used to solve classification problem