advantages and limitations of decision trees we list number of advantages and disadvantages of decision treesas compared with other supervised learning methods such as were discussed in and advantages the tree structure can handle both categorical and numerical features in natural and straightforward way specificallythere is no need to pre-process categorical featuressay via the introduction of dummy variables the final tree obtained after the training phase can be compactly stored for the purpose of making predictions for new feature vectors the prediction process only involves single tree traversal from the tree root to leaf the hierarchical nature of decision trees allows for an efficient encoding of the feature' conditional information specificallyafter an internal split of feature via the standard splitting rule ( )algorithm will only consider such subsets of data that were constructed based on this splitthus implicitly exploiting the corresponding conditional information from the initial split of the tree structure can be easily understood and interpreted by domain experts with little statistical knowledgesince it is essentially logical decision flow diagram the sequential decision tree growth procedure in algorithm and in particular the fact that the tree has been split using the most important featuresprovides an implicit step-wise variable elimination procedure in additionthe partition of the variable space into smaller regions results in simpler prediction problems in these regions decision trees are invariant under monotone transformations of the data to see thisconsider the (optimalsplitting rule ( { }where is positive feature suppose that is transformed to nowthe optimal splitting rule will take the form ( { in the classification settingit is common to report not only the predicted value of feature vectore as in ( )but also the respective class probabilities decision trees handle this task without any additional effort specificallyconsider new feature vector during the estimation processwe will perform tree traversal and the point will end up in certain leaf the probability of this feature vector lying in class can be estimated as the proportion of training points in that are in class as each training point is treated equally in the construction of treetheir structure of the tree will be relatively robust to outliers in waytrees exhibit similar kind of robustness as the sample median does for real-valued data