APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Overfitting is more likely when you have huge amount of data to train?
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TRUE
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FALSE
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Either A or B
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None of the above
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Explanation:
Detailed explanation-1: -Solutions: True. With small training dataset, it’s easier to find a hypothesis to fit the training data exactly, i.e., overfit.
Detailed explanation-2: -You are more likely to get overfitting when the set of training data is small. TRUE. It will generalize poorly when training on a small dataset. This will likely lead to overfitting.
Detailed explanation-3: -So increasing the amount of data can only make overfitting worse if you mistakenly also increase the complexity of your model. Otherwise, the performance on the test set should improve or remain the same, but not get significantly worse.
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