# Machine Learning: Models, Challenges, and Research Directions
## 3 Machine Learning Models
### Supervised Learning
can be selected according to different metrics, namely entropy, Gini index, classification error, information gain, gain ratio, and twoing criteria [58]. Tree-based models provide several benefits, such as high accuracy and ease of interpretation without any excessive data preprocessing. These models, as shown in Figure 2, consist of several supervised models, including classification and regression tree, cervical segment 4/5, cervical segment 5/0, iterative dichotomiser 3, chi-square automatic interaction detection, and Hoeffding decision tree. Instance-based learning, memory-based learning, refers to a group of ML techniques that can adapt to unseen data and are commonly used for real-time data processing. These methods use the concepts of the nearest neighbor optimization algorithm to classify or predict data. The memory-based algorithms mainly follow the simplest instance-based learning framework, instance based one (IB1), which includes three core functions, namely similarity function, concept description updater, and classification function [59]. These algorithms, based on the previous functions' description, require a large amount of memory to store the data. Memory-based models delay the processing until a new instance is classified or predicted, which is known as the lazy learning pattern. Lazy learning has the advantage of estimating the target function locally and differently for each new instance [60; 61]. As a result, instance-based methods have a lot of flexibility and multiple options when it comes to designing the objective functions. They are frequently used to build test instance models which consist of five techniques, namely radius neighbor, K-nearest neighbors, C-support vector machine, linear support vector machine, and nu-support vector machine, as shown in Figure 2.  
Regularization-based models are critical learning algorithms that are used to appropriately fit a function on the training set and prevent overfitting problems by adding some extra information to the models [62]. In these models, an additional penalty term, extra information, in the error function is added to tune the models and control the fluctuating function [63]. The reduction in the value of an error is called the shrinkage method, and it is the core aspect of the regularization-based technique. The shrinkage function may lead to variance reduction, which is very effective in large datasets, especially when the data are from a high dimensional environment [64]. Regularization-based models can be classified into ridge classifier, least absolute shrinkage and selection operator, elastic net, one rule, zero rule, stochastic gradient descent, passive aggressive classifiers, logistic regression, linear discriminant analysis, quadratic discriminant analysis, stepwise classifier, and multivariate adaptive regression spines, as shown in Figure 2.  
Neural networks, also known as artificial neural networks (ANN), are an important subset of machine learning techniques that are inspired by the human brain. These techniques can be effectively used in classification, clustering, pattern recognition, and predictions. The great potential of such techniques is to provide high-speed processing in parallel implementation. ANNs are widely used universal function approximations for different numerical paradigms due to their high rate of self-learning, adaptivity, fault tolerance, and nonlinearity features of these techniques [65; 66]. A universal function approximation refers to how a neural network can approximate any function used in its training and validation. In addition, these techniques are easy to use and more precise in comparison with techniques with large inputs [67]. Supervised neural networks can also be classified into four categories: multi-layer perceptron, convolutional neural networks, deep neural networks, and recurrent neural networks, as illustrated in Figure 2.  
Ensemble-based machine learning techniques, also known as multiplier classifier systems, train and integrate several learners to address a problem.