# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 2  
Table 5, using dataset 2, gives the result of 7 machine learning algorithms successfully trained using the dataset. Results were produced for each of the algorithms using the evaluation metrics stated earlier. Quadratic Discriminant Analysis QDA had the worst score of 0.1298. The Random Forest ensemble had the highest accuracy score of 0.8779, and KNN, Logistics Regression, and SVM all had accuracy scores of 0.8769. The same applied to the Gradient Boosting and Max Voting ensembles, both of which had an accuracy score of 0.8769. Immediately following is the Neural Network, which had an accuracy level of 0.8731, followed by the Ada Boost Ensemble, which returned an accuracy score of 0.8654. This score is relatively high, considering the margin of the other three ensemble learnings, though it seems very low compared to the first three mentioned earlier (Random Forest, Max Voting, and Gradient Boosting). Decision Tree had an accuracy level of 0.8298, which is also high on the average accuracy score. Gaussian NB had the least accuracy level of  
Figure 2: Graphical representation of Evaluation Metrics against Ensemble Learning Models for Dataset 1. 0.7981, which makes it suitable for detecting virtual harassment in comparison with the other accuracy score. In terms of other performance metrics, such as MCC and KAPPA, KNN, Logistics Regression, SVM, Max Voting, and QDA, all had the worst score of 0.0000. They all returned zero values. The Ada Boost, Gradient Boosting, Random Forest, and Max Voting ensembles all performed poorly at FDR, FNR, and FPR, respectively, as they produced results between 0.12 and 0.13. Furthermore, as shown in the table, Random Forest, which has the highest accuracy, also produced a high precision score of about 0.8779. The same applied to its recall, specificity, F1 Score, and Negative Predictive Value (NPV). Random Forest had an average score for the AUC area of 0.50. The same average score applied to all other algorithms and ensemble learning, which all have an average between 0.50 and 0.56. Figure 3 gives a clear graphical representation of the evaluation metrics, algorithms, and ensemble learning techniques using Dataset 2. The bar graph represents each of the machine learning algorithms, ensemble learning, and the accuracy results they produced across. The four ensemble learning models used in the course of this project, which include Ada Boost, Gradient Boosting, Random Forest, and Max Voting, were extensively trained using our datasets, and outputs were extracted from each of the algorithms. Each of the algorithms considering our performance metrics, as stated in chapter three, produced an output result consisting of Accuracy, Precision, Recall, Specificity, MCC, KAPPA, F1 Score, AUC, FDR, FNR, FPR, and NPV. The accuracy levels help us identify which of the classifiers performs best when it comes to detecting virtual harassment. For the ensemble classifiers in Dataset 2, Ada Boost had the lowest accuracy level at 0.8654, followed by Gradient Boosting and Max Voting, which were both slightly higher by 0.0115, each achieving an accuracy score of 0.8769. Overall, the best ensemble performer for Dataset 2 is the Random Forest ensemble, which attained an accuracy of 0.8779, making it the most appropriate in detecting a virtual harassment post in Dataset 2, although it scored poorly in FDR, FNR, and FPR, at 0.12 (12%). This, however, was not as low as MCC and KAPPA, which both returned 0.00 (0%). However, Ada Boost has the highest MCC and KAPPA of all the four ensemble learning methods, at 0.18 and 0.16 respectively, which is the sum of errors made for each example during the training and validation process. Figure 4 gives a clear graphical representation of the Evaluation metrics and the algorithms using Dataset 2. The bar graph represents each of the ensemble learning models and the results they produced across each of the evaluation metrics used.