Table 1.

Representative Machine Learning Algorithms

Algorithm	Description	Use
Logistic regression	An algorithm that estimates probability of dichotomized outcome from multiple covariates using logistic function.	Classification
Decision tree	A flow chart–like algorithm that divides data into branches by considering information gain. The final branches represent output of the algorithm (class or value).	Classification/regression
(simple) Neural network	An algorithm inspired by human brain architecture. Layers consisting of nodes are connected to one another with edges weighted as per training results.	Classification/regression
K nearest neighbor	A simple algorithm that classifies observations by comparing k examples that exist in the nearest locations (=examples with the most similar features).	Classification/regression
Support vector machine	Support vector machine draws a boundary line that maximizes margins from each class. New observations are classified using this line.	Classification/regression
K means	A clustering method that makes k clusters in which each observation belongs to the cluster that has its mean in the nearest locations from the observation.	Clustering
Hierarchical clustering	A type of cluster analysis that builds a dendrogram with a hierarchy of clusters. Pairs of clusters are merged to form clusters as they move up the hierarchy (agglomerative approach).	Clustering
Principal component analysis	An algorithm that converts high dimensional data into lower dimensional data with keeping important information as much as possible by orthogonal transformation	Dimensionality reduction