# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 1
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **MCC** & **KAPPA** & **F1 Score** & **AUC** & **FDR** & **FNR** & **FPR** & **NVP** \\ \hline Decision Trees & 0.6487 & 0.6500 & 0.6500 & 0.6500 & 0.3000 & 0.3000 & 0.6500 & 0.6500 & 0.3500 & 0.3500 & 0.3500 & 0.6500 \\ The K-Nearest & 0.5683 & 0.5700 & 0.5700 & 0.5700 & 0.1900 & 0.1200 & 0.5700 & 0.5600 & 0.4300 & 0.4300 & 0.4300 & 0.5700 \\ Logistic & 0.6923 & 0.6900 & 0.6900 & 0.6900 & 0.3900 & 0.3900 & 0.6900 & 0.6900 & 0.3100 & 0.3100 & 0.3100 & 0.6900 \\ Gaussian Naive & 0.5597 & 0.5600 & 0.5600 & 0.5600 & 0.0800 & 0.0600 & 0.5600 & 0.5400 & 0.4400 & 0.4400 & 0.5600 \\ Neural Network & 0.6827 & 0.6800 & 0.6800 & 0.6800 & 0.3600 & 0.3600 & 0.6800 & 0.6800 & 0.3200 & 0.3200 & 0.3200 & 0.6800 \\ Quadratic & 0.5923 & 0.5900 & 0.5900 & 0.5900 & 0.2100 & 0.1900 & 0.5900 & 0.6000 & 0.4100 & 0.4100 & 0.4100 & 0.5900 \\ Analysis & 0.6897 & 0.6900 & 0.6900 & 0.6900 & 0.3800 & 0.3800 & 0.6900 & 0.6900 & 0.3100 & 0.3100 & 0.3100 & 0.6900 \\ AdaBoost & 0.6227 & 0.6200 & 0.6200 & 0.6200 & 0.2900 & 0.2500 & 0.6200 & 0.6300 & 0.3800 & 0.3800 & 0.3800 & 0.6200 \\ Gradient & 0.6483 & 0.6500 & 0.6500 & 0.6500 & 0.3200 & 0.3000 & 0.6500 & 0.6500 & 0.3500 & 0.3500 & 0.3500 & 0.6500 \\ Boosting & 0.6837 & 0.6800 & 0.6800 & 0.6800 & 0.3700 & 0.3700 & 0.6800 & 0.6800 & 0.3200 & 0.3200 & 0.3200 & 0.6800 \\ Max Voting & 0.7047 & 0.7000 & 0.7000 & 0.7000 & 0.4100 & 0.4100 & 0.7000 & 0.7000 & 0.3000 & 0.3000 & 0.3000 & 0.7000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Result of Dataset 1 using the Machine Learning Algorithms and Ensemble learning methods. 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. Considering the ensemble learning models on dataset 1, the result for the Ensemble classifiers on dataset 1 began with Ada Boost, which had the lowest accuracy level at 0.6227, followed by Gradient Boosting and Random Forest, which were at 0.6483 and 0.6837, respectively. The overall best ensemble performer for Dataset 1 was the Max Voting ensemble, which attained an accuracy of 0.7047. This is still the best in detecting a virtual harassment post in Dataset 1, although it produced the lowest FDR, FNR, and FPR results, at 0.30 (30%). However, Ada Boost had the lowest MCC and KAPPA of all the four ensemble learning methods, at 0.29 and 0.25, respectively, which is the sum of errors made for each example during the training and validation process. Figure 2 gives a clear graphical representation of the Evaluation metrics and the algorithms using Dataset 1. The bar graph represents each of the ensemble learning models and the results they produced across each of the Evaluation metrics used.