## 3 Machine Learning
### Techniques and Algorithms
#### 3.1.1 Supervised Learning  
The goal of supervised ML is to build a model that create evidence-based predictions in the presence of uncertainty. These algorithms take a known data set (input) and known responses to the data (output), then train the model and generate analytical predictions in response to the new data. An example of this algorithm is used in weather forecasting. Supervised learning uses classification and regression techniques to develop predictive models. The most popular supervised machine learning methods are artificial neural networks, support vector machine, decision trees, bayesian networks, \(k\)-nearest neighbour, and hidden Markov models [26]. These algorithms are explained below:  
* Artificial neural networks (ANN) are computational brain-inspired models and interlinked by a lot of interconnections (artificial synapses) of artificial neurons (nodes) capable of performing specific calculations at their inputs [27]. An artificial neuron is composed of three or more layers, an input layer, one or more hidden layers, and an output layer. An ANN is capable of creating non-linear models to obtain the relationships between input attributes and label classification [28]. The main characteristics of ANN are adapting from experience, learning capability, generalisation capability, data organisation, fault tolerance, distributed storage, and facilitated prototyping [29]. These algorithms are useful for speech and pattern recognition [30], climate forecasting [31], and disease diagnosis [32]; although this model also solves classification and regression problems. * Support vector machine (SVM) is one of the most accurate and robust methods of ML algorithms. This classifier works by identifying a hyperplane between two classes of labelled data in a set of training data. The SVM classifier uses several types of methods, e.g., non-linearity and use of kernels, separability, and margins or risk minimisation. Non-linearity and kernel usage are some of the pioneering discoveries in the field of ML; this method permits that a non-linear problem can be transformed into a linear problem. Several types of separating hyperplanes can be realized using a kernel, such as radial basis function (RBF), polynomial, linear, or sigmoid. Risk minimisation can be applied to cases that do not fit into the traditional SVM architecture, such as problems with missing data or unlabelled data [33; 34; 35]. * Decision tree (DT) models are accurate, stable, and straightforward to interpret. Their construction is based on decision rules that are represented in the form of a tree. The result of these models can represent non-linear relationships for problem-solving. Decision trees and random forests are the most remarkable because they are more precise and elaborate. Their predictive capacity  
Figure 1: Machine learning algorithms. is higher because of these characteristics, but their performance is low. Most commonly used algorithms for building decision trees are CART (Classification and Regression Tree), ID3 (Iterative Dichotomiser), and CHAID (Chi-Squared Automatic Interaction Detector) [33; 34; 36]. * Bayesian networks (BN) are probabilistic graphical models used to describe and analyse multivariate distributions. These variables can be continuous or discrete, however, when all variables are discrete, the notation is represented as a series of sums and products. In the graphic representation of a BN, the nodes represent an observable variable or state, and the edges symbolise the conditional dependencies between the nodes. BN has been used in different areas, for example, Microsoft Windows System, NASA mission control, and bioinformatics applications [34; 37; 38]. * \(k\)-nearest neighbour (\(k\)-NN) can be used for both regression and classification problems. Due to simplicity, effectiveness, and intuitiveness of the concept, this model can be used to identify the nearest neighbours for a given data point based on a distance measure [39; 40]. The assumption is that similar elements are closer together.