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What is the right amount of data to allocate for training, validation, and test sets?

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Candidates will get this entry from top machine learning interview questions for experienced professionals. The exact amount is not possible as we have to find the perfect balance. In the case of the too-small test set, we can have unreliable estimates for model performance.

In the case of the excessive small training set, actual model parameters can have high variance. The best-recommended practice, in this case, is the 80/20 or train/test split. Subsequently, you can split the train set into train/validation splits or partitions to ensure cross-validation.

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