If a decision tree is excessively tailored to its training data, which consequence is most likely if you prune it?

💡 Explanation

Pruning reduces overfitting by simplifying the model and removing specific noise from the training data, because it reduces the model's complexity; therefore, pruning improves the model's ability to generalize to unseen data, rather than fitting the training data too closely.

• Within the context of RSA cryptography, which outcome arises when assuming a number, 'n', can be factored efficiently despite its selection following established prime number generation protocols?
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