COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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What is a way to prevent overfitting?
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Abstraction
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Decomposition
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Cross validation
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None of the above
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Explanation:
Detailed explanation-1: -Cross-validation is a robust measure to prevent overfitting. The complete dataset is split into parts. In standard K-fold cross-validation, we need to partition the data into k folds. Then, we iteratively train the algorithm on k-1 folds while using the remaining holdout fold as the test set.
Detailed explanation-2: -If you would see 1.0 accuracy for training sets, this is overfitting. The other option is: Run more splits. Then you are sure that the algorithm is not overfitting, if every test score has a high accuracy you are doing good.
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