MACHINE LEARNING
INTRODUCTION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Adding a non-important feature to a linear regression model may result in.1.Increase in MSE2.Decrease in R-square
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1
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2
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1 and 2
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none of these
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Explanation:
Detailed explanation-1: -As we increase the size of the training data, the bias would increase while the variance would decrease.
Detailed explanation-2: -Higher values of K will result in higher confidence on the cross-validation result as compared to lower value of K. If K=N, then it is called Leave one out cross validation, where N is the number of observations.
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