## 5 Results
### Performance Scores of the Greedy Methods  
As a popular feature selection model, Removing Features with Low Variance (RFLV) is especially valuable when working with numerical features. At first, it calculates the variance of each numerical feature in the dataset. Variance quantifies the dispersion or spread of data points around the mean. Features with low variance exhibit minimal or insignificant changes in their values, thereby providing less informative input for prediction. Next, we establish a threshold value to determine the minimum acceptable variance below which a feature is deemed to have a low variance. The selection of this threshold can be based on domain knowledge or through experimentation. Within the scope of the experiment, experiments were carried out by selecting four different threshold values as _default_, _0.16_, _0.21_, _0.24_. We eliminated the features that have a lower variance than the specified thresholds. The results of this technique are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Versions** & **V1** & **V2** & **V3** & **V4** \\ \hline
**Threshold** & **0** & **0.16** & **0.21** & **0.24** \\ \hline
**\# of Features** & **72** & **50** & **44** & **37** \\ \hline Logistic & Recall & 71.53 & 72.93 & 70.27 & 58.27 \\ Regression & Accuracy & 80.11 & _80.34_ & 79.71 & 75.56 \\ Random & Recall & _87.83_ & 87.59 & 87.3 & 86.24 \\ Forest & Accuracy & **91.78** & 91.67 & 91.47 & 90.5 \\ XGBoost & Recall & 77.50 & 77.59 & 77.4 & 76.07 \\ Accuracy & _85.87_ & 85.66 & 85.49 & 83.73 \\ Decision & Recall & 85.11 & _85.43_ & 84.62 & 83.32 \\ Tree & Accuracy & 87.45 & _87.97_ & 87.52 & 86.5 \\ Naive & Recall & 74.18 & 74.29 & 73.55 & 62.97 \\ Bayes & Accuracy & 75.77 & 76.09 & _76.30_ & 69.72 \\  & Recall & 80.45 & 80.33 & 80.19 & 79.76 \\ KNN & Accuracy & 87.11 & 87.01 & 86.94 & 86.45 \\  & Recall & 83.58 & 83.8 & _84.21_ & 82.81 \\ ANN & Accuracy & 88.83 & 88.82 & _89.13_ & 88.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scores of feature selection with Removing Features With Low Variance. On the other hand, RFE iteratively selects features by recursively considering smaller and smaller feature subsets and selecting the desired number of features of top rank according to their importance. The number of features to be selected for RFE was first tested by determining a wide range. Then, the number of features in the appropriate range was tested by changing the number of features in small steps. When considering the results in Table 3, the highest rates were obtained in the experiments carried out in groups with 45 or 55 features. However, with these results, it can be said that the results obtained in the experiments where random forest and XGBoost algorithms were used are very close to each other. Another common feature selection method is SFS, which is a technique that involves systematically adding or removing features from a feature subset to find the optimal set of features for a given ML task. It is a greedy search algorithm that evaluates different feature subsets based on their performance using a specified ML model and a chosen evaluation metric. Within the scope of the study, SFS experiments were performed by selecting 25, 35, 45, and 55 features. In the light of the data shared in Table 4, experiments where random forest and XGBoost algorithms were used are very close to each other in the SFS category. However, it did not give as good results as RFE. Tree-based classifiers are often used in conjunction with the SFMFS. This is because tree-based models have a built-in mechanism for measuring the importance of each feature in the dataset, which can be used by SFMFS to select the most relevant features. In addition, features are also selected using importance or coefficient attributes according to the threshold value with SFMFS. In this context, experiments were conducted on four different models. Features selected with four different algorithms (decision Tree, logistic regression, random forest and XGBoost) were used in the experiments.