quinta-feira, junho 22, 2017

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.


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