# A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection
## 4 Discussion
A decision tree is also a supervised learning algorithm; since it is based on queries, by building a specific set of questions and answers, the algorithm can gradually reach a specified level of confidence in order to provide the answer to the global question [60]. A decision tree algorithm has no prior knowledge with respect to the outside world, which is why each relationship must be taught/introduced into the system. Each node contains a piece of information gained for predicting the target value. During the implementation of the algorithm, there are two possible options at each step, \"true\" and \"false\", in order to evaluate whether a specific criterion is met. By conducting queries, the algorithm will reach some prediction that is the best fit for the relation introduced in the system. The evolution of the decision tree algorithm is a random forest. It combines a number of decision trees into one so called \"forest\" [62; 63]. Since each decision tree naturally has a random subset of features during the process of question formulation, and has access to a random set of training data points, a combination of several decision trees into the forest allows for the introduction of greater diversity, which results in a more robust prediction. For cases where we need to predict continuous variables (skin temperature for example), the random forest takes the average of all individual decision tree predictions. However, if there is a need to solve the classification problem (e.g., \"comfortable\"; \"uncomfortable\") instead of regression, the random forest will follow the majority of the vote for a predicted class [64]. Linear regression is another type of algorithm that uses supervised learning. It was developed for statistical purposes but has evolved into other fields. It was originally used to evaluate, understand and study the relationship between input and output numerical variables. On its own, simple form linear regression is shown to perform poorly in some cases due to the complexity of the real world. That is why the given algorithm evolved into ordinary least squares and gradient descent [60]. The following assumptions are made for linear regression: the relationship between input and output data is linear; data are cleaned and all noise is removed; highly correlated data should be treated since overfitting may be a big problem in given case; transforming functions may be implemented in order to make data follow the Gaussian distribution; and standardization or normalization of the parameters should be performed in order to increase the accuracy of the prediction. The k-NN is yet another supervised learning algorithm. Also named the lazy learning algorithm [63], it uses the same data for training and testing. The k-NN does not introduce any assumptions about the provided data set, which makes it non-parametric [65]. It is usually helpful for classification problems such as decision support systems. Figure 7: General scheme of data flow among case studies  
ANN can be both a supervised and an unsupervised learning algorithm, depending on the purpose of the neural network and its architecture. ANN is a group of nodes connected with each other in a way that mimics brain behavior and function [60,65]. The deep neural network, which was used in the study by [PERSON] et al. [40], represents a more complex architecture of neural network layers. Multiple layers were used to extract higher level features from the raw data. All those algorithms were used to predict whether a person is comfortable or not within provided indoor conditions. In general, it is a good approach for the given task, but, as was mentioned before in the brief description of each algorithm, overfitting is a significant problem that may occur in each algorithm's implementation [60,63]. Only a few articles described how they approached this problem [31,35,40,43,53,55].