# LSRM: A New Method for Turkish Text Classification
## 3 Materials and Methods
### Performance and Statistical Metrics  
As provided in Table 2, a confusion matrix table is used to evaluate the generated classification models. In the table, while the rows correspond to the actual class label, the columns indicate the prediction results [49].  
It is necessary to use accuracy (ACC), probability of detection (PD), precision, and F-measure metrics to measure the performance of the models. ACC is a commonly used metric to identify the class discrimination ability of the classifier. It can be defined as the percentage of test examples correctly classified by the algorithm. ACC is one of the fundamental metrics used to assess the classifier performance [49]. Equation (1) is the formula to calculate the ACC ratio. ACC determines the ratio of the number of correctly predicted data to the total number of data. A high ACC value indicates how well the model makes a prediction.  
\[\text{ACC}=\frac{\text{(TP+TN)}}{\text{Pozitif}+\text{Negatif}} \tag{1}\]  
Probability of Detection (PD): It involves the calculation of the negative value ratio, also called \"recall\". The value ratio is calculated by dividing the true negative value by the sum of the false negative and true negative values using the formula in Equation (2) [49].  
It is a metric that measures the probability that a model will correctly identify a certain class. Especially in sensitive applications, the PD metric plays a critical role in cases to minimize false negatives (failure to detect a certain class). A high PD value indicates that the model can reliably determine a certain class, whereas a low PD value denotes difficulty in effectively identifying the correct class.  
\[\text{PD}=\text{TN}/\text{(FN+TN)} \tag{2}\]  
Precision: Its calculation involves dividing the true positive value by the sum of the true positive and false positive values based on the formula in Equation (3) [49]. A high precision value indicates that the model can accurately identify a certain class, whereas a low precision value means that the model is prone to misclassification.  
\[\text{Precision}=\text{TP}/\text{(TP+FP)} \tag{3}\]  
F-measure (FM): It is ideal to test the accuracy of the prediction, especially when the data are unbalanced. F1-Score is the harmonic average of the precision and recall values calculated by the formula in Equation (4) [49]. The F-score is a metric calculated by combining precision and recall values. It allows us to assess a model's precision and recall values simultaneously. Hence, we may evaluate how well the model optimizes both measures.  
\[\text{FM}=2\times((\text{Recall}\times\text{Precision})/(\text{Recall+Precision })) \tag{4}\]  
ANOVA: Analysis of variance (ANOVA) is a statistical analysis technique used to identify differences among groups. ANOVA calculates the intra-group and inter-group variations and determines the differences among these variabilities. ANOVA test values include the Adjusted Sum of Squares (Adj SS), Adjusted Mean Square (Adj MS), F-value, and \(p\)-value. Adj SS stands for the variance rate of the model. It indicates how well the model fits the data. Adj MS denotes the variance adjusted based on the rate variance and the degree of freedom of the model. It demonstrates how the factors in the model explain the variation in the data. F-value is statistical data used to test the significance level of the model. It displays whether the factors in the model have a significant effect on the data. The \(p\)-value is a crucial metric in the ANOVA test. It is a statistical value used to test the accuracy of a hypothesis. \(p\)-value helps determine whether the results under a hypothesis  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Estimated Class**} \\ \hline  & **Prediction No (\(-\))** & **Prediction Yes (\(+\))** \\ \hline Predicted True & TN & FP \\ Predicted False & FP & TP \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
in the dataset are explicitly coincidental or different. This value must be less than 0.05 to consider the hypothesis true [33].