Decision trees have played a significant role in data mining and machine learning since the 1960′s. They generate white-box classification and regression models which can be used for feature selection and sample prediction. The transparency of these models is a big advantage over black-box learners, in that the models are easy to understand and interpret, and that they can be readily extracted and implemented into any programming language (with nested if-else statements) for use in production environments. Furthermore, decision trees require almost no data preparation (i.e. normalization) and can handle both numerical and nominal/categorical data. Decision trees can also be … Keep reading →