nextbuild the model with the help of following scriptmodel baggingclassifier(base_estimator=cartn_estimators=num_treesrandom_state=seedcalculate and print the result as followsresults cross_val_score(modelxycv=kfoldprint(results mean()output the output above shows that we got around accuracy of our bagged decision tree classifier model random forest it is an extension of bagged decision trees for individual classifiersthe samples of training dataset are taken with replacementbut the trees are constructed in such way that reduces the correlation between them alsoa random subset of features is considered to choose each split point rather than greedily choosing the best split point in construction of each tree in the following python recipewe are going to build bagged random forest ensemble model by using randomforestclassifier class of sklearn on 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 randomforestclassifier nowwe need to load the pima diabetes dataset as did in 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