## 4 Results  
In machine learning classification, the results are measured on the basis of accuracy, recall, precision, F-measure, ROC, and root mean square error (RMSE). Accuracy is the ratio of correct predictions of the sample over the total number of predictions. The results may vary among DM models due to internal changes in processing functionality. All of the evaluation metrics are built on four types of classifications: true positives (_TP_), true negatives (_TN_), false positives (_FP_), and false negatives (_FN_). \[Accuracy=\frac{No.of\ correct\ predictions}{Total\ No.of\ predictions} \tag{25}\]  
For binary classification, the accuracy is measured using Equation (25) or Equation (26):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{26}\]  
_TP_ represents true positive, _TN_ is a true negative, _FP_ is false positive, and _FN_ represents false negative. _AUC-ROC_ is also used to calculate the performance of multi-classification problems. _ROC_ is a probability curve that stands for Receiver Operating Characteristics, and Area under the Curve (_AUC_) measures the degree of separability. It states the capability of the model to distinguish between classes, and the higher the _AUC_, the better the model distinguishes between classes. The _ROC_ curve is plotted with _TPR_ against _FPR_, where _TPR_ is on the _y_-axis and _FPR_ is on the _x_-axis. True positive rate (_TPR_) or recall value is calculated through Equation (27):  
\[Recall\ (TPR)=\frac{TP}{TP+FN} \tag{27}\]  
False positive rate (_FPR_) is calculated through Equation (28):  
\[FPR=\frac{FP}{FP+TN} \tag{28}\]  
Precision is used to determine the number of predicted positive instances correctly classified by the algorithm as presented in Equation (29). Figure 2: The basic working mechanism of a multiclass artificial neural network. \[Precision=\frac{TP}{TP+FP} \tag{29}\]  
F-measure is used to represent the harmonic mean between two parameters, precision and recall, as shown in Equation (30). A high value of F-measure indicates that both precision and recall are reasonably high. \[F-measure=\frac{2*\left(Recall*Precision\right)}{Recall+Precision} \tag{30}\]  
_RMSE_ is a frequently used measure of the differences between values predicted by a model and observed values. The _RMSE_ represents the sample standard deviation of the differences between predicted values and observed values. where \(y_{i}^{{}^{\prime}}\) is the predicted value and \(y_{i}\) is the true value for subject \(i\). The _RMSE_ values are calculated through Equation (31):  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}^{{}^{\prime}}-y_{i}\right)^{2}}{n}} \tag{31}\]  
As ML models grow rapidly, they also need more tuning and configuration. This tuning often comes in the form of hyperparameters. The hyperparameter describes all parameters that have to be determined before the actual process of fitting a model to the data is started. These hyperparameters exist because data-based models are designed to work in different scenarios, requiring both algorithm and model modifications. In the past, these modifications were often performed by using domain knowledge or rules of thumb. However, hyperparameters are generally challenging to set. Hyperparameters in machine learning describe variables that modify how a particular model is derived from data. These parameters can modify the algorithm that performs this process, but they can also be a model parameter that the algorithm cannot reasonably determine. Most model parameters are determined through training by applying the machine learning algorithm to the data. Hyperparameters are usually not independent of each other. The number of possible combinations of hyperparameters increases exponentially with the number of hyperparameters. Because training machine learning models is computationally expensive, the main goal is to find good or optimal points with as few function evaluations as possible. A common hyperparameter in the neural network case is the learning rate.