Table 1.

This table shows the main characteristics of different types of machine learning approaches.

Type of ML	Mechanism	Type of Data Provided	Tasks for which the Mechanism can be used	Examples of Models
Supervised learning (SL)	The algorithm, provided with tuples (x,y) of input (labeled) and output (unlabeled), infers the relations that map the data.	Labeled data	Classification task; Regression task	Logistic regression Decision Tree Random Forest Support Vector Machines Artificial Neural Networks
Unsupervised learning (UL)	The algorithm exhibits self-organization, to capture hidden patterns in data.	Unlabeled data	Clustering Association; Anomalies detection	Hierarchical clustering K-mean Artificial Neural Networks
Semi-supervised learning (SSL)	The algorithm is placed between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data).	Mostly unlabeled data, with a small amount of labeled data.	Transductive task (infer the correct labels for the given unlabeled data) or inductive tasks (infer the correct mapping from x to y).	Generative model Self-training model Co-training model Transductive model Graph-based model
Reinforcement learning (RL)	The algorithm is created with a goal and a set of rules. The algorithm tends to maximize the "reward function" or reinforcement signals, to achieve the goal.	Not needing labeled input/output pairs to be presented; only a numerical performance score is given as guidance.	Good for modeling complex-task decision-making processes, such as economics and game theory under bounded rationality.	Monte Carlo methods Q-learning State–action–reward–state–action methods