# Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
## 5 Discussion
### Data Analysis for Dataset 4  
Table 7 gives the result of the 7 Machine algorithms as well as their ensemble learning counterpart using Dataset 4. Logistics Regression and SVM have the same highest accuracy level of 0.8383, respectively. Neural Network has an accuracy of 0.8083. Decision Tree had accuracy results of 0.7797, followed by Gaussian NB, with an accuracy of 0.6517, KNN and QDA also had accuracy results of 0.5577 and 0.5287, respectively, which is on the average level. The best matrices for Dataset 4 are Accuracy, Precision, Recall, Specificity, F1 score, and AUC. The second-best matrices are MCC and KAPPA, followed by the worst, which are FDR, FNR, and FPR. Table 7 also has other Evaluation metrics results; QDA on the other hand didn't do so well across the 12 performance matrices. We can see clearly from Table 7, that the QDA obtained an accuracy of 0.5287, precision, recall, specificity, F1 score, and NPV of 0.53 (53%) respectively, while FNR, FDR, and FPR all attained a score of 0.47 (47%), AUC had the best score of 0.54 (54%), MCC and KAPPA obtained the worst score at 0.09 (9%) and 0.07 (7%) respectively. This result implies that only one evaluation metric cannot fully be enough to predict the efficiency of an algorithm, except tested with other metrics. Figure 7 gives a clear graphical representation of the Evaluation metrics and the Algorithms and ensemble learning using Dataset 4. The Bar graph represents each of the machine learning algorithms and the ensemble learning techniques and the results they produced. \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 & 0.7797 & 0.7800 & 0.7800 & 0.7800 & 0.5600 & 0.5600 & 0.7800 & 0.7800 & 0.7800 & 0.2200 & 0.2200 & 0.7800 \\ K-Nearest & 0.5577 & 0.5600 & 0.5600 & 0.5600 & 0.2100 & 0.1000 & 0.5600 & 0.5500 & 0.4400 & 0.4400 & 0.5600 \\ Neighbor & & & & & & & & & & & & \\ Logistic & 0.8383 & 0.8400 & 0.8400 & 0.8400 & 0.6800 & 0.6800 & 0.8400 & 0.8400 & 0.1600 & 0.1600 & 0.1600 & 0.8400 \\ regression & Gaussian Naive & & & & & & & & & & & & \\ Bayes & 0.6517 & 0.6500 & 0.6500 & 0.6500 & 0.3100 & 0.3100 & 0.6500 & 0.6500 & 0.3500 & 0.3500 & 0.3500 & 0.6500 \\ Neural Network & 0.8083 & 0.8100 & 0.8100 & 0.8100 & 0.6200 & 0.6200 & 0.8100 & 0.8100 & 0.1900 & 0.1900 & 0.1900 & 0.8100 \\ Quadratic & & & & & & & & & & & & & \\ Discriminant & 0.5287 & 0.5300 & 0.5300 & 0.5300 & 0.0900 & 0.0700 & 0.5300 & 0.5400 & 0.4700 & 0.4700 & 0.4700 & 0.5300 \\ Analysis & & & & & & & & & & & & & \\ SVM & 0.8383 & 0.8400 & 0.8400 & 0.8400 & 0.6800 & 0.6800 & 0.8400 & 0.8400 & 0.1600 & 0.1600 & 0.1600 & 0.8400 \\ AdaBoost & 0.7980 & 0.8000 & 0.8000 & 0.8000 & 0.6100 & 0.5900 & 0.8000 & 0.8000 & 0.2000 & 0.2000 & 0.2000 & 0.8000 \\ Gradient & 0.8010 & 0.8000 & 0.8000 & 0.8000 & 0.6200 & 0.6000 & 0.8000 & 0.8000 & 0.2000 & 0.2000 & 0.2000 & 0.8000 \\ Boosting & 0.8210 & 0.8200 & 0.8200 & 0.8200 & 0.6500 & 0.6400 & 0.8200 & 0.8200 & 0.1800 & 0.1800 & 0.1800 & 0.8200 \\ Max Voting & 0.8280 & 0.8300 & 0.8300 & 0.8300 & 0.6600 & 0.6500 & 0.8300 & 0.8300 & 0.1700 & 0.1700 & 0.1700 & 0.8300 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of Dataset 4 using the Machine Learning Algorithms and Ensemble learning methods. Figure 6: Graphical representation of Evaluation Metrics against Ensemble Learning Models for Dataset 3. The four ensemble learning models--Ada boost, Gradient Boosting, Random Forest, and Max Voting--used in this study were thoroughly trained using our datasets, and results were obtained from each of the algorithms. Each algorithm that took into account the chapter three performance measures generated an output result that included Accuracy, Precision, Recall, Specificity, MCC, KAPPA, F1 Score, AUC, FDR, FNR, FPR, and NPV. The accuracy levels allow us to identify the ensemble classifiers that perform the best at identifying virtual harassment.