Figure 6.

(a) Decision trees assign labels (leafs) to a given sample by going through a multi-level structure where different features (root nodes) and solutions (branches) are tested. (b) In a Random Forest algorithm, decision trees are combined, following an ensemble learning approach, which enables to get more accurate predictions than a single tree. Each individual tree in the forest spits out a class prediction and the class with the most votes becomes the final model’s prediction.