COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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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.What will happen when you fit degree 4 polynomial in linear regression?
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There are high chances that degree 4 polynomial will over fit the data
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There are high chances that degree 4 polynomial will under fit the data
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Can’t Predict
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None of these
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Explanation:
Detailed explanation-1: -We can choose the degree of polynomial based on the relationship between target and predictor. The 1-degree polynomial is a simple linear regression; therefore, the value of degree must be greater than 1.
Detailed explanation-2: -For figure with polynomial degree=3, the curve fits perfectly with the training data points. So, we would have a very low error value and this would also be termed as Low Bias.
Detailed explanation-3: -Which of the following is true when you fit degree 2 polynomial? Since a degree 2 polynomial will be less complex as compared to degree 3, the bias will be high, and the variance will be low.
Detailed explanation-4: -R’s lm function creates a regression model.
There is 1 question to complete.