# Credibility Analysis of User-Designed Content Using Machine Learning Techniques
## 4 Methodology
### Algorithm  
Algorithms used for the regression deliver the accuracy, and weight to each algorithm required. The model should be further balanced to avoid overtraining hence, balancing is used to maintain the equilibrium needed for the gradient descent. The efficiency of the algorithm is extended by minimizing the value of the curve. The gradient descent is decreased to its lowest value (Algorithm 1). The flow of the data can be well interpreted with the help of the diagram mentioned in Figure 2. The unique contribution of the work can be seen in weight calculation, error minimization, and optimization during the regression process. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sr. No.** & **Algorithm** & **Accuracy in \%** \\ \hline
1 & Linear Regression & 65 \\
2 & Lasso Regression & 67 \\
3 & Ridge Regression & 66 \\
4 & Decision Tree & 71 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Individual regression algorithm at level zero (L-0). ``` Step 1: Training Models--Train k base regression models (M = M1, M2,..., Mk) using training sets X and Target Output Y \(y_{ij}\) be the predicted output of \(j^{\text{th}}\) model for \(i^{\text{th}}\) Sample. Step 2: Error Calculation--Objective Function is defined as follows \[I(\theta)=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}\ -\theta_{j}\times y_{ij}\ \right)^{2}+\mu\sum_{j=1}^{k}\ \theta_{j}^{2}\] (1)

where \(\theta=\left[\theta_{1},\ \theta_{2},\ \theta_{3}\ \ldots\ \theta_{n}\right]\theta_{i}\) is a weight associated with model M \({}_{i}\). \(\mu\) is a regularization parameter used to control the selected value of \(\theta\) by an optimization algorithm. Step 3: Minimization Optimization problem can be defined as follows \[Min_{\theta}\ I(\theta)=Min_{\theta}\ \left\{\frac{1}{n}(\sum_{i=1}^{n}\left(y_{i}\ -\theta_{j}\times y_{ij}\ \right))^{2}+\mu\sum_{j=1}^{k}\theta_{j}^{2}\right\}\] (2) \[\text{Where}\ 0\ \leq\ \theta_{j}\ \leq\ 1\ \ \ \sum_{j=1}^{k}\ \theta_{j}=1\]

To obtain the values of \(\theta_{j}\) by solving the equation mentioned above Step 4: Testing the dataset

For test dataset \(x_{test}=\left[x_{1},\ x_{2},\ x_{3},\ \ldots\ x_{n}\right]\) predicted value of \(\hat{y}_{i}\) for ith sample in \(x_{test}\) as follows \[\hat{y}_{i}=\frac{1}{n}\sum_{j=1}^{k}\left(\theta_{j}\times y_{ij}\right)\] (3)

where \(\hat{y}_{ij}\) is the predicted value for \(i\)\({}^{\text{th}}\) sample \(x_{i}\), by \(j\)\({}^{\text{th}}\) base model M

Figure 2: Data flow diagram FERMO algorithm. Figure 2 indicates the flow of the data from input collected from the dataset to the final outcome. ## 5 Regression Loss

When the regression problem is converted into a classification problem, the accuracy of classification can be verified with Precision, Recall, F-Measure, MAP, etc. To assess the performance of the regression there is no direct assessment available [36]. The error loss is calculated to know the exactness of the prediction, which is also called an R2 score. In this experiment losses include Mean Squared Error, Mean Absolute Error, Mean Absolute Logarithmic Error, Mean Absolute Percentage Error, Huber loss, LogCosh, and Quantile loss. ### Mean Squared Error (MSE) L2 Loss, Quadratic Loss

Sum of squared distance between expected variable and the target variable. \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{4}\]

Graph MSE Loss Vs Prediction (\"U\" shape). MAE is robust for outliers, but learning is not smooth or good because of the absence of a curve. The mean is more susceptible to outliers than the median. Use MSE when the loss of data is costlier than the presence and absence of an outlier. ### Mean Absolute Error (MAE)

Summation of the absolute difference between the predicted and the target variable [2]. If we consider the direction, then it is Mean Bias Error (sum of residuals/errors). MAE loss Vs Predictions (\"V\" shape)

\[MAE=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y}_{i}\rvert \tag{5}\]

The value of the gradient remains the same throughout; the learning is difficult.