YikaiLL/DataMiningConceptTech.md

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DataMiningConceptTech.md

Chapter 8 Classfication

8.5 Model Evaluation and Selection

8.5.4 Bootstrap

Unlike the accuracy estimation methods just mentioned, the bootstrap method samples the given training tuples uniformly with replacement.

.632 boostrap

$$(1-1/d)^d=1-e^{-1}=0.632$$ 63.2% will form the training set and 36.8% would be at the test set. Bootstrapping tends to be overly optimistic. It works best with small data sets.

Bootstrapping tends to be overly optimistic. It works best with small data sets.