Editing Statistical classification (section)

==Algorithms==

Since no single form of classification is appropriate for all data sets, a large toolkit of classification algorithms has been developed. The most commonly used include:<ref>{{Cite news|url=https://builtin.com/data-science/tour-top-10-algorithms-machine-learning-newbies|title=A Tour of The Top 10 Algorithms for Machine Learning Newbies|date=2018-01-20|work=Built In|access-date=2019-06-10}}</ref>

* {{annotated link|Artificial neural networks}}
* {{annotated link|Boosting (machine learning)}}
* {{annotated link|Random forest}}
* {{annotated link|Genetic programming}}
** {{annotated link|Gene expression programming}}
** {{annotated link|Multi expression programming}}
** {{annotated link|Linear genetic programming}}
* {{annotated link|Kernel estimation|text=Variable kernel density estimation#Use for statistical classification}}
** {{annotated link|k-nearest neighbor algorithm|k-nearest neighbor}}
* {{annotated link|Learning vector quantization}}
* {{annotated link|Linear classifier}}
** {{annotated link|Fisher's linear discriminant}}
** {{annotated link|Logistic regression}}
** {{annotated link|Naive Bayes classifier}}
** {{annotated link|Perceptron}}
* {{annotated link|Quadratic classifier}}
* {{annotated link|Support vector machine}}
** {{annotated link|Least squares support vector machine}}

Choices between different possible algorithms are frequently made on the basis of quantitative [[Classification#Evaluation of accuracy|evaluation of accuracy]].