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Intro to Deep Learning : LR QUIZ

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Intro to Deep Learning: Linear Regression Quiz

Test your knowledge on Linear Regression and its applications in the realm of Deep Learning. This quiz covers fundamental concepts, evaluation metrics, and techniques such as regularization, helping you solidify your understanding of Linear Regression.

Join now to explore the following:

True or False questions for quick assessment
Multiple choice questions that challenge your expertise
Evaluation metrics related to continuous output

10 Questions2 MinutesCreated by LearningMind321

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

True

False

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

Accuracy

Mean-Squared-Error

True-False: Lasso Regularization (L1) can be used for variable selection in Linear Regression.

TRUE

FALSE

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

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

TRUE

FALSE

In Ridge Regression (L2) : What will happen when you apply very large penalty?

Some of the coefficient will become absolute zero

Some of the coefficient will approach zero but not absolute zero

Both A and B depending on the situation

None of these

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

Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? 1) Add more variables 2) Start introducing polynomial degree variables 3) Remove some variables

1 and 2

2 and 3

1 and 3

1, 2 and 3

L2 < l1 < l3

l1 > l2 > l3

L1 = l2 = l3

None of these

What would be the root mean square training error for this data if you run a Linear Regression model of the form (Y = A0+A1X)?

Less than 0

Greater than zero

Equal to 0

None of these

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