## 2 Experimental Approach
### Evaluation Metrics  
Analyzing prediction techniques' performance is crucial. The Root Mean Square Error (RMSE) is the standard deviation of prediction errors, as stated by [PERSON] and [PERSON] [48]. It uses the regression line to calculate its distance from the data points and the extent to which they have been extended. Given that regression analysis and prediction applications commonly employ RMSE, a low value is expected [49]. How closely the regression line matches the actual data values is indicated by R-Squared (R\({}^{2}\)) [50]. R\({}^{2}\) value is supposed to be close to 1 and is between 0 and 1. The average of the absolute discrepancies between goal values and predictions is known as the mean absolute error, or MAE, as stated by [PERSON] and [PERSON] [48]. The MAE is expected to have a low value because of its linear value, which suggests that every variation has an equal influence on the mean.  
Furthermore, the statistical method known as cross-validation (CV) [51] is employed in evaluating the effectiveness of machine learning algorithms. Because CV is easy to use and comprehend, machine learning frequently uses it to benchmark and select a model for a specific predictive modeling assignment. Furthermore, it often produces forecasts with smaller errors [52] when compared to other approaches. When forecasting using information not utilized in the model's training, the effectiveness of a machine learning  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ML** & **Advantages** & **Disadvantages** \\ \hline ABR & Straightforward and simple to use & Sensitive to anomalies and erratic data \\  & Less likely to overfit than individual models & Able to be considerate of the preferences of weak learners \\  & Selects features automatically & \\ \hline DTR & Able to comprehend and interpret & Inclined to overfitting, particularly in deep tree \\  & Able to handle both category and numerical data without requiring a lot of preparation & High volatility, maybe poor generalization to new data \\  & Robust against anomalies and missing values & Small data changes might cause instability and lead to noticeably altered tree architectures. \\ \hline SVR & Efficient in areas with several dimensions & Requires that the kernel function and regularization parameters be chosen carefully \\  & Effective use of memory for sparse data & Possibly sensitive to feature scaling \\  & Flexible, diverse kernel functions may be applied to represent different kinds of connections. & Interpretability is less than with linear models \\ \hline GBR & To reduce overfitting, use an ensemble of weak learners, usually decision trees & Sensitive to overfitting if the hyperparameters are incorrectly set \\  & handles a variety of data formats and scales effectively to big datasets & Computationally costly in comparison to certain other techniques \\  & includes feature significance, which facilitates the interpretation & Needs several hyperparameters, like learning rate and tree depth, to be carefully adjusted \\ \hline RFR & Robust to noisy and overfitting data & Less comprehensible than separate decision trees \\  & Effectively manages big datasets and high-dimensional data & When training large forests, computation may be expensive \\  & Automatically assesses the feature importance & Possibly not very good with extrapolation jobs \\ \hline XGBR & Scalable, effective, and capable of handling & Hyperparameter tweaking sensitive; meticulous optimization is needed \\  & Techniques for regularization lessen overfitting & Less comprehensible than more \\  & Cross-validation built-in and early & For huge datasets, it may require a lot of RAM stop mechanisms \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._model on hypothetical data is used to forecast the model's overall performance [51]. The ten- and five-fold CVs are frequently used.