Intro to Deep Learning : LR QUIZ
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
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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