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Regularization is introduced in class as a numeric term that can be incorporated into a loss function. This is straightforward for algorithms such as neural networks that seek to optimize a numeric loss function. Here, explain possible ways regularization can be used to fine tune other types of algorithms, specifically decision trees and naive Bayes classifiers.

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