The Success of Decision Tree

The success of decision trees is explained by several factors that make them quite attractive in practice:

• Decision trees are non-parametric. They can model arbitrarily complex relations between inputs and outputs, without any a priori assumption;
• Decision trees handle heterogeneous data (ordered or categorical variables, or a mix of both);
• Decision trees intrinsically implement feature selection, making them robust to irrelevant or noisy variables (at least to some extent);
• Decision trees are robust to outliers or errors in labels;
• Decision trees are easily interpretable, even for non-statistically oriented users.