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Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions.

Supervised learning includes two categories of algorithms:

* '''Classification''': for categorical response values, where the data can be separated into specific “classes”

* '''Regression''': for continuous-response values

== Classification ==

* Support vector machines (SVM)
* Neural networks
* Linear classifiers: a group of algorithms such as:
** Logistic regression
** Perceptron
** Fisher's linear discrimination
** Na?ve Bayes classifier
* [[Machine learning/Supervised Learning/Decision Trees|Decision trees]]: a group of algorithms such as
** Random forest
** Bootstrap aggregation
** Boosting
* Discriminant analysis
* Nearest neighbors (''k''NN): A ''Non-parametric'' and ''instance-based'' method used for classification and regression

== Regression ==

* Linear regression
* Nonlinear regression
* Generalized linear models
* Decision trees: a group of algorithms such as
** Random forest
** Bootstrap aggregation
** Boosting
* Neural networks

[[Category:Machine learning|Supervised Learning]]