Binary decision trees

A binary decision tree is a structure based on a sequential decision process. Starting from the root, a feature is evaluated and one of the two branches is selected. This procedure is repeated until a final leaf is reached, which normally represents the classification target we're looking for. Considering other algorithms, decision trees seem to be simpler in their dynamics; however, if the dataset is splittable while keeping an internal balance, the overall process is intuitive and rather fast in its predictions. Moreover, decision trees can work efficiently with unnormalized datasets because their internal structure is not influenced by the values assumed by each feature. In the following figure, there are plots of an unnormalized bidimensional dataset and the cross-validation scores obtained using a logistic regression and a decision tree:

The decision tree always achieves a score close to 1.0, while the logistic regression has an average slightly greater than 0.6...