## 3 Methods
### Machine Learning Models
#### 3.3.2 Automatic Classification Model to Predict Qualitative Parameters  
The Automatic machine learning(AutoML) system was adopted to find the best ML model for our framework. H2O AutoML is an open source, user-friendly machine learning software that was designed not only for advanced machine learning users, but also for non experts. Recent studies show that H2O AutoML [43] performs better than other competitor tools. The authors of [44,45] assessed the robustness and efficiency of AutoML, with respect to other automatic models such as TPOT [46] and AutoKeras [47]. These authors used dirty, clean and noisy data sets to evaluate the robustness of the tool. Other studies, [48,49] have shown the effectiveness of the AutoML system, with respect to other tools, like auto-sklearn [50] and Auto-WEKA [51] using open source datasets. AutoML relies on the efficient training of H2O machine learning algorithms to produce a large number of models in a short time. H2O AutoML supports the supervised training of regression, binary classification and multi-class classification models on tabular datasets. H2O AutoML is available in Python, R, Java and Scala as well as through a web GUI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Description** & **Formulation** \\ \hline Residual Standard Error (RSE) & The Residual Standard Error is a measure of the quality of a linear regression fit. & \(RSE=\sqrt{\frac{1}{n-2}RSS}\) \\  & R squared is the square of the Pearson correlation coefficient between the labels and the predicted values. This metric ranges from zero to one. A higher value indicates a higher quality model. This measures the proportion of variation explained by only those independent variables that really help to explain the dependent variable. In the equation, where \(R^{2}\)-sample R-square; p-Number of predictors; N-total sample size & \\  & This makes it possible to compute the variance of the dependant variable, the simpler model is not able to explain as much as the complex model. In the equation \(k_{1}\) and \(k_{2}\) are parameters of two models & \\  & This is a statistical test that determines the probability of extreme results of the statistical hypothesis test, and which takes the Null Hypothesis to be correct. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics of the linear regression model.  
The base models of H2O are Generalized Linear Models (GLM), Distributed Random Forests (DRF), XGBoost, Gradient Boosting Machines (GBM), and Deep Learning (NN). The used hyperparameters are chosen from a predefined search space using a grid search. H2O chooses one of the three different options. It may use just one of the base models or their hyperparameter-optimised versions. It can also choose a Best Of Family Stacked Ensemble model, which includes one model from each category and the last available option is the All Models Stacked Ensemble pipeline. After training one of the above mentioned models, AutoML uses a test dataset to evaluate the accuracy and quality of the new model, and provides a number of evaluation metrics that indicate how good the model performs on the test dataset. The following metrics were selected to evaluate the regression model:  
The evaluation metrics used for the classification model are shown in the Table 3.