TABLE 1.

Examples of machine-learning algorithms

	Example	Description
Supervised learning algorithm
Regularised regression	LASSO	An extension of classic regression algorithms in which a penalty is enforced to the fitted model to minimise its complexity and reduce the risk of overfitting
Tree-based model	Classification and regression trees, random forest, gradient boosted trees (XGBoost)	Based on decision trees (a decision support tool which is a sequence of “if-then-else” splits are derived by iteratively separating data into groups based on the relationship between attributes and outcomes)
Support vector machine	Linear, hinge loss, radial basis function kernel	Represents data in a multidimensional feature space and fits a “hyperplane” that best separates data based on outcomes of interest
KNN	KNN	Represents data in a multidimensional feature space and uses local information about observations closest to a new dataset to predict outcomes for the new dataset
Neural network	Deep neural networks, ANNs	Nonlinear algorithms built using multiple layers of nodes that extract features from the data and perform combinations that best predict outcomes
Unsupervised learning algorithm
Dimensionality reduction algorithms	Principal component analysis, linear discriminant analysis	Exploits inherent structure to transform data from high-dimensional space into a low-dimensional space which retains some meaningful attributes of the original data
LCA	LCA	Identifies hidden population subgroups (latent classes) in the data. Used in datasets with complex constructs that have multiple behaviours. The probability of class membership is indirectly estimated by measuring patterns in the data
Cluster analysis	K-means, hierarchical cluster analysis	Uses inherent structures in the data to best organise data into subgroups of maximum commonality based on some distance measure between features
Reinforcement learning algorithm
Reinforcement learning	Markov decision process and Q learning	Provides tools to optimise sequences of decision for the best outcomes or to maximise rewards. Learns by trial and error. An action is reinforced with the action that results in a positive outcome (reward), and vice versa. The algorithm can improve performance in scenarios where a learning system can choose to repeat known decisions (exploitation) or make novel decisions expecting to gain even greater rewards (exploration)

KNN: K-nearest neighbour; LCA: latent class analysis; LASSO: least absolute shrinkage and selection operator; ANN: artificial neural network.