Linear Regression Review

True-False: Linear Regression is a supervised machine learning algorithm.

• True

• False

Which of the following methods do we use to find the best fit line for data in Linear Regression?

• Maximum Likelihood

• Logarithmic Loss

• Least Square Error (LSE)

• Mean Absolute Error (MAE)

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

• Root Mean Square Error (RMSE)

• All the above

True-False: Lasso Regularization can be used for variable selection in Linear Regression.

• True

• False

Which of the following is true about Residuals ?

• Lower is better

• Higher is better

• A or B depend on the situation

• None of these

Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data.

You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.

Which of the following is true for X1?

• Relation between the X1 and Y is weak

• Relation between the X1 and Y is strong

• Relation between the X1 and Y is neutral

• Correlation can’t judge the relationship

Suppose Pearson correlation between X1 and X2 is zero. In such case, is it right to conclude that X1 and X2 do not have any relation between them?

• True

• False

Which of the following offsets, do we use in linear regression’s least square line fit? Suppose horizontal axis is independent variable and vertical axis is dependent variable.

• Perpendicular offset

• Vertical offset

• Both, depending on the situation

• None of above

True- False: Overfitting is more likely when you have huge amount of data to train?

• True

• False

Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with penalty (λ) aka (α)

• In case of very large (λ); bias is low

• In case of very large (λ); bias is high

• We can’t say about bias

• None of these