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% Here the training data is adapted from UCI ML repository, 'zoo' data
dtreeCPD=tree_CPD;
% load data
fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'zoo', 'zoo1.data')
data=load(fname);
data=data';
data=transform_data_into_bnt_format(data, []);
% learn decision tree from data
ns=2*ones(1,17);
ns(13)=6;
ns(17)=7;
dtreeCPD1=learn_params(dtreeCPD,1:17,data,ns,[],'stop_cases',5); % a node with less than 5 cases will not be splitted
% evaluate on data
[score,outputs]=evaluate_tree_performance(dtreeCPD1,1:17,data,ns,[]);
fprintf('Accuracy in old training data %6.3f\n',score);
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