Intoduction to Machine Learning (Day-13)

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?

• Least Square Error

• Maximum Likelihood

• Logarithmic Loss

• None of these

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

• AUC-ROC

• Preceision

• Mean-Squared-Error

• None of these

Suppose that we have N independent variables (X1,X2… Xn) and a 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

Which of the following statement is true about outliers in Linear regression?

• Linear regression is sensitive to outliers

• Linear regression is not sensitive to outliers

• Can’t say

• None of these

Suppose that you have a data set D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.

What will happen when you fit degree 4 polynomial in linear regression?

• There are high chances that degree 4 polynomial will over fit the data

• There are high chances that degree 4 polynomial will under fit the data

• Can’t say

• None of these

Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is “0” or in another terms it perfectly fits the data.

Which of the following is true when you fit degree 2 polynomial?

• Bias will be high, variance will be high

• Bias will be low, variance will be high

• Bias will be high, variance will be low

• Bias will be low, variance will be low

In least squares regression, which of the following is not a required assumption about the error term ε?

• The expected value of the error term is one

• The variance of the error term is the same for all values of x

• The values of the error term are independent

• The error term is normally distributed.

In regression analysis, if the independent variable (X) is measured in kilograms, the dependent variable (Y)

• must also be in kilograms

• must be in some unit of weight

• cannot be in kilograms

• can be any units

Which problem can be solved using linear regression?

• Prediction of price of a house based on size of room

• Prediction of height based on weight

• Both

• None of these