instituto de matemáticas universidad de sevilla
Antonio de Castro Brzezicki
Supervised Classification: Random Forests
Seminario PHD

Many problems in the real life can be modelled as classification problems: the early detection of diseases or the granting of credit to a certain individual, among others. Supervised Classification handles this issue by learning from a sample, with the aim of being able to infer the class of forthcoming observations. Random Forests [1] is one of the most popular Supervised Classification techniques; it is but a collection of Classification Trees [2] on which randomness is applied somehow. Along this talk, we will mainly focus on how Classification Trees are built. Last, future research in this area will be discussed.

[1] Leo Breiman. “Random forests.” Machine Learning, 45(1):5–32, 2001.

[2] Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. “Classification and regression trees.” CRC press, 1984.

[3] Trabajo Fin de Máster. Universidad de Sevilla. Máster Universitario en Matemáticas. María Cristina Molero del Río. “Aprendizaje supervisado mediante Random Forests.”