# Credibility Analysis of User-Designed Content Using Machine Learning Techniques
## 4 Methodology
### Algorithm
The smaller value of the delta Huber function resembles MSE and for higher values, it resembles MAE [16]. ### Mean Square Error Loss & Mean Absolute Error Loss

Relative plotting of the graph of Mean Square Error Loss & Mean Absolute Error Loss together, helps in understanding the errors explored using a different perspective [12; 43]. In Figure 6 the area under the Mean Square Error Loss is smaller than the area covered by Mean Absolute Error Loss. This is proof of the logical derivation of the square function. ### Mean Square Error Loss & Huber Loss

Figure 7 indicates the plotting of Mean Error loss and Huber Loss. The representation of the Mean Square Error Loss plotted with Mean Absolute loss and Huber Loss looks similar to that shown in Figures 8 and 9, but there is a difference between the scales used in both graphs. The details can be seen in the relative error loss in Figure 7. Figure 7 indicates the increased smoothness for the value of delta [3; 8]. There are a lot of variants, which can be studied by varying the delta value. For the constant value of 0.5, the plotting represents the minima close to the equilibrium. This proves that there is no further training or backpropagation is needed to achieve the minima separately [44]. ### Mean Absolute Error Loss & Huber Loss

Figure 8 represents Mean Absolute Error and Huber loss. The association helps in depicting the minimum loss at the intersection of these graphs. ### Mean Square Error Loss, Mean Absolute Error Loss & Huber Loss

Figure 9 represents the Huber Loss function with Mean Square Error Loss, Mean Absolute Error Loss can be seen at the side of the smoothness of a loss function curve [45]. This represents that there are fewer outliers available. The area under the curve is the smallest in MSE and the largest in MAE. The Huber Loss function is between the MSE and MAE represented with orange color [29; 46]. Individual regression accuracies of Linear Regression, Lasso Regression, Ridge Regression, Decision Tree are 65%, 67%, 66%, 71%, respectively. Ensemble regression algorithms XGBoost, Gradient Boost, Adaboost, random Forest, Bagging Regressor give accuracies equal to 86%, 84.22%, 79%, 81%, 83.52%, 96.29%, respectively. ## 7 Conclusions

The FERMO algorithm delivers an accuracy, which is higher in comparison with all other individual regression algorithms and their combined versions, this accuracy is considerably higher than the approaches considered for the comparison of the performance. This enhancement is because the two unique features of the algorithm are: (i) load distribution amongst the regression algorithms and (ii) the identification of the weight factor. The validation mechanism used is exploring all types of possible losses in form of the regression. Even if there are any linear or non-linear losses, all of them are highlighted with this loss analysis. Minimum loss incurred in the process of the regression indicates maximum accuracy. The experiment proves that the accuracy is acceptable. The accuracy can be improvised by minimizing the precision loss, here training cannot be employed otherwise the model will suffer from overtraining and thus the values will be away from the gradient descent. Hence, balancing factors are used instead of further training the model. Conceptualization, M.G., R.J., S.J. and K.K. ([PERSON]); methodology, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, S.G., S.R. and A.K. ; validation, M.S. ; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, A.K., M.S., [PERSON] ([PERSON]) and M.G. All authors have read and agreed to the published version of the manuscript. This research was funded by \"Research Support Fund of Symbiosis International (Deemed University), Pune, Maharashtra, India\". Not Applicable.