## 3 Results and Discussions
### Models and Metrics Used
#### 3.2.2 Unsupervised Learning Algorithms  
Unsupervised learning algorithm takes into account the aim of finding hidden patterns from the input data, provided output labels do not exist. The major concentrations of the authors were found in clustering, [PERSON]'s inequality, mixture of Gaussians, and expectation maximization. Most of the articles on unsupervised learning tried to attain their pattern of clustering by finding patterns using dimension reduction techniques. These dimension reduction techniques find the variance maximizing directions onto which to project the data. Some of the metrics used to evaluate the clustering are the Davies-Bouldin index, popularly known as DB index, which calculates the average distance of all points in a particular cluster from the cluster centroid, and the Dunn index that calculates the ratio between the minimum inter-cluster distance to the maximum intra-cluster distance. The Dunn index showed an increase as the performance of the model improved.  
Many ensemble learning and reinforcement learning algorithms were taken into account in the survey made. Even though all the articles taken for study could not be illustrated, some of the models are given in brief note. For better understanding of the models used, they are best illustrated in Figure 5a,b.  
The articles that contribute to the categorization are shown in Tables A1-A4 of the Appendix A. Based on the quality score, the articles are segregated. Even though there exist some discrepancies with the presence of metrics in the articles, they were also considered for quality assessment, where they obtained a minimal score. The evaluation metrics or the principles used were not found in most of the articles, which could not provide a proper insight on the data or the model used.  
The entire set of the selected articles were evaluated and analyzed with respect to the performance metrics, which gives a response to RQ2. The set of algorithms, as discussed in the previous section, takes up the major category based on the way those algorithms function as classification and regression, and in some articles, an ensemble of classification and regression algorithms were used. The usage of articles in these categories is analyzed as shown in Figure 7.  
A classification problem is the one where the dependent variable is a categorical one. Classification models entail algorithms such as logistic regression, DT, random forest, and naive Bayes. Model performance metrics are estimated based on the values obtained from confusion matrix, accuracy score, classification report, receiver operating characteristic (RoC), and area under curve (AUC), confusion matrix being an intuitive metrics to determine the accuracy of the given model is suitable for a multiclass classification problem. The performance metrics with respect to the classification algorithms taken up for study are listed in Table 6.  
Confusion matrix depicts the overall performance of the model, and accuracy reveals the number of correct predictions made by the model.  
Regression problems are the ones wherein we find a linear relationship between the target variables and the predictors. In such problems, the target variable holds a continuous value. Such methods are typically used for forecasting. Regression models include algorithms such as linear regression, DT, random forest, and SVM.  
The performance metrics of the regression problems are identified as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R-squared  
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