Classification rules
To discover classification rules for our dataset, starting from angiogenesis-related SNPs and identified SNPs in ADME genes (sorafenib dataset), we applied a classifier for the identification of rules which could facilitate the stratification of responder/non-responder patients in terms of sorafenib response. Classifiers belong to the non-parametric supervised learning algorithm category, where the machines learn patterns buried in the data using previously labeled training data which is mandatory for supervised learning. Supervised learning aims to produce a model that predicts class variable values by learning simple patterns inferred from the data features. The RandomTree’s output is a classification tree easily understandable even by non-domain experts which can be quickly translated into classification rules in the ”IF (cond1 & cond2 & … & condn) THEN class” format.