COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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It could make model underfit
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It could make model overfit
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Either A or B
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None of the above
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Detailed explanation-1: -“the random oversampling may increase the likelihood of overfitting occurring, since it makes exact copies of the minority class examples.
Detailed explanation-2: -Overfitting occurs when the model cannot generalize and fits too closely to the training dataset instead. Overfitting happens due to several reasons, such as: The training data size is too small and does not contain enough data samples to accurately represent all possible input data values.
Detailed explanation-3: -The main disadvantage with oversampling, from our perspective, is that by making exact copies of existing examples, it makes overfitting likely. In fact, with oversampling it is quite common for a learner to generate a classification rule to cover a single, replicated, example.
Detailed explanation-4: -Typically, if there are not enough samples in the training data set, especially if the number of samples is less than the number of model parameters (count by element), overfitting is more likely to occur. Additionally, as we increase the amount of training data, the generalization error typically decreases.