error rate will be close to 50%. (c) The .632 bootstrap method.
While the .632 bootstrap approach is useful for obtaining a reliable estimate
of model accuracy, it has a known limitation. Consider a two-class problem,
where there are equal number of positive and negative examples in the data.
Suppose the class labels for the examples are generated randomly. The clas-
sifier used is an unpruned decision tree (i.e., a perfect memorizer). Determine
the accuracy of the classifier using each of the following methods.
The training error for a perfect memorizer is 100% while the error rate
for each bootstrap sample is close to 50%. Substituting this information
into the formula for .632 bootstrap method, the error estimate is:
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