The following is a training dataset that has ten one dimensional objects. Person id Home owner Refund
1 Yes TRUE
2 No TRUE
3 Yes FALSE
4 No FALSE
5 Yes FALSE
6 No TRUE
7 NO TRUE
8 NO FALSE
9 NO FALSE
10 YES FALSE
A. Create ten bootstrap samples from the above training dataset. (You should use R to extract bootstrap samples. Use person id to extract the samples)
B. Build a decision stump (One level decision tree that split the root using entropy) for each bootstrap sample to predict if each object/person gets refund based on the marital status homeowner attribute.
C. Apply the decision stumps on the original dataset to predict a class label (refund) for each objects in the original data set.
D. Determine the final prediction for each object using majority of votes.
E. Find the training error of your model. * You should not use R for section B, C,D, and E - Please show your works

i think its d if im not mistaken

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