## 9 Evaluation Methods and Results
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} \\ \cline{3-8}  & & **Cyberbullying** & **Non-CB** & **Cyberbullying** & **Non-CB** & **Cyberbullying** & **Non-CB** \\ \hline SVM & Tfidf + Uni & 0.83 \(\pm\) 0.02 & 0.83 \(\pm\) 0.01 & 0.78 \(\pm\) 0.03 & 0.88 \(\pm\) 0.02 & 0.80 \(\pm\) 0.02 & 0.85 \(\pm\) 0.02 \\ NB & Tfidf + Uni & 0.81 \(\pm\) 0.04 & 0.76 \(\pm\) 0.03 & 0.64 \(\pm\) 0.02 & 0.88 \(\pm\) 0.03 & 0.71 \(\pm\) 0.03 & 0.81 \(\pm\) 0.03 \\ LR & Tfidf + Uni & 0.80 \(\pm\) 0.05 & 0.80 \(\pm\) 0.06 & 0.73 \(\pm\) 0.04 & 0.86 \(\pm\) 0.02 & 0.76 \(\pm\) 0.04 & 0.83 \(\pm\) 0.02 \\ DT & Uni + Tri & 0.74 \(\pm\) 0.03 & 0.78 \(\pm\) 0.02 & 0.72 \(\pm\) 0.04 & 0.80 \(\pm\) 0.03 & 0.73 \(\pm\) 0.03 & 0.79 \(\pm\) 0.02 \\ AdaBoost & Uni + Tri & 0.65 \(\pm\) 0.05 & 0.75 \(\pm\) 0.06 & 0.71 \(\pm\) 0.04 & 0.70 \(\pm\) 0.03 & 0.68 \(\pm\) 0.04 & 0.72 \(\pm\) 0.04 \\ XGBoost & Tfidf + Uni & 0.76 \(\pm\) 0.02 & 0.80 \(\pm\) 0.03 & 0.74 \(\pm\) 0.01 & 0.82 \(\pm\) 0.02 & 0.75 \(\pm\) 0.02 & 0.81 \(\pm\) 0.02 \\ Bagging & Tfidf + Uni & 0.77 \(\pm\) 0.03 & 0.78 \(\pm\) 0.02 & 0.70 \(\pm\) 0.02 & 0.84 \(\pm\) 0.03 & 0.73 \(\pm\) 0.02 & 0.81 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation of proposed models based on precision, recall, and F1 measure. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature Engineering** & \multicolumn{6}{c}{**Approach**} \\ \hline  & **SVM** & **NB** & **LR** & **DT** & **Ada Boost** & **XGBoost** & **Bagging** \\ Tfidf + Unigrams & **0.83**\(\pm\) 0.01 & **0.78**\(\pm\) 0.04 & **0.80**\(\pm\) 0.03 & **0.76**\(\pm\) 0.02 & **0.70**\(\pm\) 0.03 & **0.79**\(\pm\) 0.04 & **0.78**\(\pm\) 0.03 \\ Tfidf + Bigrams & 0.69 \(\pm\) 0.03 & 0.68 \(\pm\) 0.05 & 0.71 \(\pm\) 0.04 & 0.68 \(\pm\) 0.02 & 0.58 \(\pm\) 0.04 & 0.61 \(\pm\) 0.02 & 0.68 \(\pm\) 0.03 \\ Tfidf + Trigrams & 0.64 \(\pm\) 0.01 & 0.65 \(\pm\) 0.02 & 0.67 \(\pm\) 0.03 & 0.64 \(\pm\) 0.03 & 0.58 \(\pm\) 0.03 & 0.57 \(\pm\) 0.05 & 0.65 \(\pm\) 0.02 \\ Unigram + Bigrams & **0.83**\(\pm\) 0.02 & 0.77 \(\pm\) 0.04 & 0.79 \(\pm\) 0.05 & **0.76**\(\pm\) 0.02 & **0.70**\(\pm\) 0.05 & 0.76 \(\pm\) 0.03 & 0.77 \(\pm\) 0.04 \\ Unigram + Trigrams & 0.68 \(\pm\) 0.04 & **0.78**\(\pm\) 0.05 & 0.79 \(\pm\) 0.03 & **0.76**\(\pm\) 0.01 & **0.70**\(\pm\) 0.02 & 0.76 \(\pm\) 0.04 & 0.76 \(\pm\) 0.02 \\ Bigrams + Trigrams & 0.68 \(\pm\) 0.02 & 0.71 \(\pm\) 0.03 & 0.70 \(\pm\) 0.04 & 0.67 \(\pm\) 0.02 & 0.59 \(\pm\) 0.01 & 0.61 \(\pm\) 0.03 & 0.68 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy comparison of proposed models. Most significant results are in bold. We also visualized the performance of the proposed models using the AUC metric, the area under the receiver operating characteristics (ROC) curve. It is a metric used to assess a classification model's performance at different thresholds. In binary classification, the thresholds are various probability cutoffs that separate the two classes. It employs probability to use the model's capacity to distinguish between classes. Figure 13 presents the AUC curve of the proposed models for the Roman Urdu dataset at hybrid N-Grams and tfidf unigrams. We can observe from the figure that the proposed models show significantly  
Figure 12: Precision–Recall curves for the proposed algorithms at highest accuracy level in each case. (**a**) SVM (Tfidf + unigrams). (**b**) Naive Bayes (Tfidf + unigrams). (**c**) Logistic regression (Tfidf + unigrams). (**d**) Decision tree (unigrams + trigrams). (**e**) AdaBoost (unigrams + trigrams). (**f**) Xboost (Tfidf + unigrams). (**g**) Bagging classifier (Tfidf + Uni). cant results are bold.