APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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You observe the following while fitting a linear regression to the data:As you increase the amount of training data, The train error is quite low (almost what you expect it to), while the test error is much higher than the train error. What do you think is the main reason behind this behavior. Choose the most probable option
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High variance
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High model bias
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None of the above
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Both A & B
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Explanation:
Detailed explanation-1: -Linear regression performs poorly when there are non-linear relationships. Linear regression assumes that the data points are not independent (i.e. One observation might be affected by another).
Detailed explanation-2: -Variance is more harmful to test error in machine learning.
There is 1 question to complete.