# Credibility Analysis of User-Designed Content Using Machine Learning Techniques
## 4 Methodology
### Algorithm
A small value causes a change in the gradient, which degrades the learning. Use MAE when outliers cannot be tolerated [9]. ### Huber Loss Function, Smooth Mean Absolute Error

Error is a small quadratic function. Delta determines the smaller value of error. Training the delta parameter in the iteration is a problem. This combines the robustness of MAE and decreased minima of MSE [15]. Problem--Missing minima because of a large curve. \[L_{\delta}(y,f(x))=\left\{ \begin{array}{ll}\frac{1}{2}(y-f(x))^{2},&for\ \lvert y-f(x)\rvert\leq\delta\\ \delta\ \lvert y-f(x)\rvert-\frac{1}{2}\delta^{2},&x\geq 0\end{array} \right. \tag{6}\]

### LogCosh Loss

It is similar to the squared error, it is not affected by wrong predictions. It is twice differentiable but may become affected by large outliers [13]. \[L(y,y^{p})=\sum_{i=1}^{n}\log(\cosh(y_{i}{}^{p})-y_{i}) \tag{7}\]

### Quantile Loss

The difference between the Actual and expected value is constant [37; 38; 39]. Instead of exact prediction, the interval prediction gives more accurate. The non-linear model is more practical. It is an extension to MAE. Half quantile is MAE [17]. \[L_{\gamma}(y,y^{p})=\sum_{i=y_{i}<y_{i}^{p}}(\gamma-1).\left|y_{i}-y_{i}^{p} \right|+\sum_{i=y_{i}<y_{i}^{p}}(\gamma).\left|y_{i}-y_{i}^{p}\right| \tag{8}\]

## 6 Results

The result section covers the accuracy and error loss in actual and expected output. Minima adhered to a sharp curve represents the favorable results. These errors can be minimized, in the Optimization section, the details about the minima and enhancement are addressed. Results received after the experimentation are represented with nine different graphs presented in Figures 3-9. Figure 4: Mean Absolute Error (MAE). Figure 3: Mean Squared Error (MSE)—MR. Figure 5: Huber loss. Figure 6: Mean Square Error loss & Mean Absolute Error loss. Figure 7: Mean Square Error Loss & Huber loss. Result analysis is carried out considering two different perspectives or levels. At level zero L-0 there are individual algorithms--Decision Tree, Ridge, lasso, Linear Regression algorithm, details are given in Tables 5 and 6. Similarly, some important ensemble techniques are used to calculate the accuracy of the regression algorithm. The percentage of the prediction is more in L-1 in comparison to L-0. The accuracy of the FERMO is 96.29%. To understand the error quotient in the prediction, loss is also calculated. The details of loss analysis during the ensemble regression are as below. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No.** & **Algorithm** & **Accuracy in \%** \\ \hline
1 & XGBoost & 86 \\
2 & Gradient Boost & 84.22 \\
3 & Adaboost Regressor & 79 \\
4 & Random Forest & 81 \\
5 & Bagging Regressor & 83.52 \\
6 & FERMO & 96.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ensemble regression algorithm at level zero (L-1). Figure 8: Mean Absolute Error loss & Mean Absolute Error & Huber Loss

Figure 9: Mean Square Error loss, Mean Absolute Error loss & Huber loss. ### Mean Squared Error (MSE)

It is the square of the difference between the actual and expected outcome. Analysis of this error helps in minimizing the effect of overall loss. Figure 5 indicates the regression loss calculation using MSE. Ideally, it must represent the U shape. FERMO is resilient to the loss of data. The curve, which is flatter is generally away from the minima [40; 41]. Figure 3 represents the MSE plotting for the movie dataset instead of the Movie Review. The sharpness in the curvature can be easily noticed. This indicates the influence of a large volume of data on a curvature. ### Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is the average of the difference between the expected and actual outcome. This is robust to the outlier this means only magnitude is covered, there is no coverage of direction [42]. ### Huber Loss Function

Huber loss function is less sensitive to the outliers, so there is no sharp angle at minima. This is also called a smooth function. This function uses the robustness of MAE and MSE.