
Intoduction to Machine Learning (Day-13)
Authored by Suresh Raikwar
Computers
University
Used 103+ times

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10 questions
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1.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
Linear Regression is a supervised machine learning algorithm
True
False
2.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
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
3.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
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
4.
MULTIPLE CHOICE QUESTION
2 mins • 1 pt
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
5.
MULTIPLE CHOICE QUESTION
1 min • 1 pt
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
6.
MULTIPLE CHOICE QUESTION
2 mins • 1 pt
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
7.
MULTIPLE CHOICE QUESTION
2 mins • 1 pt
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
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