Bootstrap Bootstrap Works well with small data sets Samples the given training tuples uniformly with replacement n i.e., each time a tuple is selected, it is equally likely to be selected again and re-added to the training set a Several bootstrap methods and a common one is. 632 bootstrap o Adata set with d tuples is sampled d times, with replacement resulting in a training set of d samples. The data tuples that did not make it into the training set end up forming the test set. about 63.2%of the original data end up in the bootstrap and the remaining 36.8%form the test set(since(1-1/d)d=e1=0.368) o Repeat the sampling procedure k times, overall accuracy of the model: ACC( M) Xi=10.632*Acc(Mi)testset +0.368* Acc TrainsetBootstrap ◼ Bootstrap ◆ Works well with small data sets ◆ Samples the given training tuples uniformly with replacement  i.e., each time a tuple is selected, it is equally likely to be selected again and re-added to the training set ◼ Several bootstrap methods and a common one is .632 bootstrap ◆ A data set with d tuples is sampled d times, with replacement, resulting in a training set of d samples. The data tuples that did not make it into the training set end up forming the test set. About 63.2% of the original data end up in the bootstrap, and the remaining 36.8% form the test set (since (1-1/d) d =e-1=0.368) ◆ Repeat the sampling procedure k times, overall accuracy of the model: 𝐴𝑐𝑐(𝑀) = 1 𝑘 σ𝑖=1 𝑘 (0.632 ∗ 𝐴𝑐𝑐(𝑀𝑖 )𝑡𝑒𝑠𝑡𝑠𝑒𝑡 + 0.368 ∗ 𝐴𝑐𝑐 𝑀𝑖 𝑡𝑟𝑎𝑖𝑛𝑠𝑒𝑡 )