Fig. 4

Three most common machine learning (ML) techniques. A machine learning model can be thought of as a complex web of interconnected nodes. Setting up an ML model involves two different kinds of data types: in the first step, training data are used to train the model. Once the model is set up in terms of its internal parameters, an unknown test dataset is used in a second step to validate the model. Finally, the model is used on new data. A Supervised learning problems can be sub-grouped into classification and regression techniques. In supervised learning, labelled data are used to train the model. This means that labelled input data are associated with a known outcome. The model is then trained on these data by an iterative process until fine-tuning of the model has been achieved. The model thus learns which features define the input data and how to identify them. This is done by applying weights, which represent numerical values assigned to connection nodes of the model. Weights determine the strength of these individual connections in the web of interconnected nodes and as such how strongly the output of a node influences another node’s input. Predictions made by supervised models can either be discrete or continuous. A model that produces discrete output data is a classification model (e.g., the result: tumour malignant or benign), and one that produces continuous output data is a regression model (e.g., the tolerable dose of a certain medication). B Unsupervised learning is used, e.g., clustering. Here, raw unlabelled data objects (on the left side) are provided as input. Training the model is also an iterative process. The results of unsupervised learning are often different clusters (as shown here with the non-overlapping geometrical shapes on the right). Clustering algorithms are used to assort the given data into groups that share common structures or patterns. C Reinforcement learning differs from supervised and unsupervised learning. In reinforcement learning, the model learns by the interactions between a decision maker/agent and its surrounding environment. The decision maker/agent selects an action according to its policy. Depending on the nature of the change in the environment, this action can be positive ("reward") which would reinforce the previous behaviour of the model, or negative ("punishment"). The goal of the model is to maximise its rewards