# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 3 Implementation and Results
\begin{table}
\begin{tabular}{l l} \hline Accuracy & RFSVM \textgreater{} SVM \textgreater{} RF \textgreater{} NB \textgreater{} XGB \\ \hline Precision & RFSVM \textgreater{} SVM \textgreater{} RF \textgreater{} XGB \textgreater{} NB \\ \hline Recall & RFSVM \textgreater{} SVM \textgreater{} RF \textgreater{} NB \textgreater{} XGB \\ \hline F1-score & RFSVM \textgreater{} SVM \textgreater{} XGB \textgreater{} NB \textgreater{} RF \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of Performance Results. Figure 8: Comparison among evaluation parameters of different classifiers. RFSVM's combination of Support Vector Machine and Random Forest is around 10% better than the naive model in terms of accuracy, which is around 6% to 8% better in terms of accuracy compared to other models such as XGBoost, Random Forest, and SVMs. (b) Analysis of different types of news categories with five different models in terms of precision in Table 7:  
RFSVM's combination of Support Vector Machines and Random Forest is around 8% better than the naive model in terms of precision, which is around 4% to 6% better in terms of precision compared to other models such as XGBoost, Random Forest, and SVMs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Avg.** & **Reference** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **[RFSVM]** \\ \hline Agriculture & 0.87 & 0.89 & 0.91 & 0.95 & 0.97 & 0.95 \\ \hline Aviation & 0.49 & 0.51 & 0.53 & 0.61 & 0.63 & 0.68 \\ \hline Sports & 0.55 & 0.57 & 0.59 & 0.65 & 0.67 & 0.72 \\ \hline Roads & 0.64 & 0.66 & 0.68 & 0.72 & 0.74 & 0.80 \\ \hline Residential & 0.53 & 0.55 & 0.57 & 0.61 & 0.63 & 0.61 \\ \hline Forest & 0.57 & 0.59 & 0.61 & 0.61 & 0.64 & 0.65 \\ \hline Village & 0.42 & 0.44 & 0.46 & 0.53 & 0.58 & 0.57 \\ \hline Finance & 0.87 & 0.89 & 0.91 & 0.95 & 0.97 & 0.95 \\ \hline Politics & 0.87 & 0.89 & 0.91 & 0.91 & 0.93 & 0.91 \\ \hline Technology & 0.42 & 0.44 & 0.46 & 0.53 & 0.55 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy values for different news categories with different ML algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Classifier Used** & **Year** & **Accuracy** & **Precision** & **F1-score** \\ \hline
[31] & NB & 2020 & 0.60 & 0.59 & 0.72 \\  & RF & & 0.59 & 0.62 & 0.67 \\  & LR & & 0.65 & 0.69 & 0.75 \\  & PAC & & 0.92 & 0.93 & 0.9257 \\ \hline
[32] & XGBoOST & 2020 & 0.75 & - & - \\  & SVM & & 0.73 & - & - \\  & RF & & 0.73 & - & - \\ \hline
[33] & SVM & 2021 & 0.8933 & - & - \\  & DT & & 0.7333 & - & - \\  & NB & & 0.8689 & - & - \\  & LR & & 0.9046 & - & - \\  & KNN & & 0.8998 & - & - \\ \hline Proposed Approach & RFSVM & & 0.9756 & 0.8821 & 0.9350 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comprehensive comparative analysis with baseline studies. RFSVM's combination of Support Vector Machines and Random Forest is around 18% better than the naive model in terms of recall, which is around 12% to 16% better in terms of recall compared to other models such as XGBoost, Random Forest, and SVMs. Analysis of different types of news categories with five different models in terms of precision in Table 9:  
RFSVM's combination of Support Vector Machines and Random Forest is around 15% better than the naive model in terms of F1-scores, which is further around 8% to 12% better in terms of F1-scores compared to other models such as XGBoost, Random Forest, and SVMs.