Question: Using training data to derive a classifier and then estimate the accuracy of the resulting learned model can result in misleading overoptimistic estimates due to

Using training data to derive a classifier and then estimate the accuracy of the resulting learned model can result in misleading overoptimistic estimates due to overspecialization of the learning algorithm to the data. Instead, it is better to measure the classifier

s accuracy on a test set consisting of class

-

labeled tuples that were not used to train the model.

True

False

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