Decision trees are used in a wide variety of areas such as radar signals, medical diagnostics, expert systems, remote sensing and voice recognition, to name just a few, successfully. Its ability to break a difficult decision-making procedure into a collection of simpler decisions and thus provide a solution is the most important feature of Decision Trees (Safavian & Landgrebe, 1991). The advantages of decision trees include: it enables to conduct feature selection implicitly, to discovering interactions and nonlinear relationships, it only requires little effort to prepare data without variable scaling, there is a reasonable number of missing values and it is not affected by outliers, it is easy to understand and to explain, it enables creating rules to formalize experts’ knowledge, decision tree can be taken as a preprocessing step in the division of a dataset into small subsets, decision trees generate a simple if-then statements, its subsets are purer than the original data set in relation to the target variable (Floares, Calin & Manolache, 2016; Kim, 2016).
We have drawn decision trees for classification problem using German credit data and for a regression problem to predict housing SalePrice (this dataset is taken from Kaggle and attached to this discussion with the R script used to produce the plots).
Floares, A. G., Calin, G. A., & Manolache, F. B. (2016, June). Bigger Data Is Better for Molecular Diagnosis Tests Based on Decision Trees. In International Conference on Data Mining and Big Data (pp. 288-295). Springer, Cham.
Kim, K. (2016). A hybrid classification algorithm by subspace partitioning through semi-supervised decision tree. Pattern Recognition, 60, 157-163.
Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE transactions on systems, man, and cybernetics, 21(3), 660-674.