Figure 9. a, b.

Over-simplified illustrations of decision tree and random forest. In panel (a), decision tree simply creates the most accurate and simple decision points in classification of the instances, providing the most interpretable models for the humans; x, z, and w represent features. In panel (b), to increase the stability and generalizability of the classifications, decision tree algorithm can be iterated several times with various methods. One of the well-known examples is the random forest classifier.