Supervised learning: Classification

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionarypeer-review

17 Citations (Scopus)


Supervised learning is the task of building a model that is able to fit the available observations. In the area of supervised learning, classification is one of the most studied problems. Given a set of predefined class labels (two or more) and a set of available observations, the aim is to build a model based on the features of the observations that is able to assign each observation to the corresponding class. In Bioinformatics several problems can be formulated as a classification task. This article introduces several supervised learning techniques that are commonly used to address a classification problem, presenting the most used measures to evaluate the performance of a classification model.

Original languageEnglish
Title of host publicationEncyclopedia of Bioinformatics and Computational Biology
Subtitle of host publicationABC of Bioinformatics
EditorsShoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach
Number of pages8
ISBN (Electronic)9780128114322
ISBN (Print)9780128114148
Publication statusPublished - 2019


  • Bioinformatics
  • Classification
  • Machine learning
  • Supervised learning


Dive into the research topics of 'Supervised learning: Classification'. Together they form a unique fingerprint.

Cite this