The capability to model unkown complex interactions between variables made machine learning a pervasive tool in bioinformatics and computational biology. Among others, decision trees and random forests have been used within the field for several successful applications achieveing high levels of performance. Furthermore, decision trees and random forests offer the possibility of inspecting the decision rules and to investigate the relevance of each variable, as well as, the dependencies among them. Here we review the theoretical foundations of both decision trees and random forests and illustrate basic case-studies as well as applications from recent literature.

Decision trees and random forests

Fratello M.;Tagliaferri R.
2018-01-01

Abstract

The capability to model unkown complex interactions between variables made machine learning a pervasive tool in bioinformatics and computational biology. Among others, decision trees and random forests have been used within the field for several successful applications achieveing high levels of performance. Furthermore, decision trees and random forests offer the possibility of inspecting the decision rules and to investigate the relevance of each variable, as well as, the dependencies among them. Here we review the theoretical foundations of both decision trees and random forests and illustrate basic case-studies as well as applications from recent literature.
9780128114322
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4761837
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