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How Can You Choose a Classifier Based on a Training Set Data Size?

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When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overfit. 

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.

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