voting in this ensemble learning modelmultiple models of different types are built and some simple statisticslike calculating mean or median etc are used to combine the predictions this prediction will serve as the additional input for training to make the final prediction bagging ensemble algorithms the following are three bagging ensemble algorithmsbagged decision treeas we know that bagging ensemble methods work well with the algorithms that have high variance andin this concernthe best one is decision tree algorithm in the following python recipewe are going to build bagged decision tree ensemble model by using baggingclassifier function of sklearn with decisiontreeclasifier ( classification regression trees algorithmon pima indians diabetes dataset firstimport the required packages as followsfrom pandas import read_csv from sklearn model_selection import kfold from sklearn model_selection import cross_val_score from sklearn ensemble import baggingclassifier from sklearn tree import decisiontreeclassifier nowwe need to load the pima diabetes dataset as we did in the previous examplespath " :\pima-indians-diabetes csvheadernames ['preg''plas''pres''skin''test''mass''pedi''age''class'data read_csv(pathnames=headernamesarray data values array[:, : array[:, nextgive the input for -fold cross validation as followsseed kfold kfold(n_splits= random_state=seedcart decisiontreeclassifier(we need to provide the number of trees we are going to build here we are building treesnum_trees