Avinash123

Hi, Bootstrapping sample is also one of method to predict result with accuracy, So1st start is Bootstrap sampling, this refers that sample taken from given population using simple random sampling. Eg 1000population We taken 100sample out of 1000 population Again suppose took 5th observation not from sample rather we take it from population So from this example we can say Boot strap sample allows duplicate data also. Eg2-1000 population We took sample out of this S1 500observation S2 500obs S3 500obs . . S100 500obs. From each observation we built a model on training data and we get different model o/p. Now since it's next case of ensemble learning so we again take decision on majority voting if it is classification problem and if it is regression we can take avg of model o/p. Since we aggregate the o/p of different model so we call it as "Bagging". One good part of Bootstrap sampling is we can skip all preprocessing statistical steps. Rather we can conclude based on majority voting result. It also have high accuracy as aggregation is happening from different model o/p.