Editing Binary classification (section)

==Statistical binary classification==
[[Statistical classification]] is a problem studied in [[machine learning]] in which the classification is performed on the basis of a [[classification rule]].  It is a type of [[supervised learning]], a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories.  When there are only two categories the problem is known as statistical binary classification.

Some of the methods commonly used for binary classification are:
* [[Decision tree learning|Decision trees]]
* [[Random forests]]
* [[Bayesian network]]s
* [[Support vector machine]]s
* [[Artificial neural network|Neural networks]]
* [[Logistic regression]]
* [[Probit model]]
* [[Genetic Programming]]
* [[Multi expression programming]]
* [[Linear genetic programming]]

Each classifier is best in only a select domain based upon the number of observations, the dimensionality of the [[feature vector]], the noise in the data and many other factors. For example, [[random forests]] perform better than [[Support vector machine|SVM]] classifiers for 3D point clouds.<ref>{{Cite journal|title = Automatic Identification of Window Regions on Indoor Point Clouds Using LiDAR and Cameras|last = Zhang & Zakhor|first = Richard & Avideh|date = 2014|journal = VIP Lab Publications|citeseerx = 10.1.1.649.303}}</ref><ref>{{Cite journal |title = Simplified markov random fields for efficient semantic labeling of 3D point clouds|last = Y. Lu and C. Rasmussen|date = 2012|journal = IROS|url=http://nameless.cis.udel.edu/pubs/2012/LR12/yan_iros2012.pdf}}</ref>