## 3 Results and Discussion
### The Performances of ML Algorithms
#### 3.2.1 The Effect of Categorical Features on the Performance of Regression Problem
Normalization is especially effective on features with different scales. If all the features are of the same size, normalization may be unnecessary. Another reason could be the normalization sensitivity of the ML algorithms. Some algorithms such as LR may not be affected much by normalization or standardization because these kinds of algorithms are based on linear relationships. The data show similar relationships regardless of scale. Algorithms such as SVM and KNN can be greatly affected by scaling as they rely on distance calculations. If there is no performance increase in these algorithms, the data are already in the optimal range. Overall, a decrease or no change in model performance may be due to the fact that the normalization technique is not suitable for the distribution of the dataset. Figure 4: Comparison of the results of performance metrics of applied ML: Black color represents the results without using categorical features, red color represents the results using categorical features after 10-fold cross validation. (**a**) R\({}^{2}\), (**b**) RMSE and (**c**) MAE. 2.3 The Effect of Applying Feature Selection (FS) Techniques to Categorical Variables on Regression Problem Performance  
Since the properties of the boring media and boring method are important in the prediction of the boring thickness value, the most distinctive features were selected among 56 features and their effect on the performance of the ML models was analyzed. Two different FS techniques were applied, including FS techniques based on correlation coefficients and the CfsSubsetEval algorithm based on the greedy approach. Since both techniques are filtering-based feature selection methods, the feature selection step is performed independently of the training phase. Two sorts of subset features were generated for FS techniques. Firstly, a total of 10 features were selected with the feature selection method based on the correlation coefficient. While six of these features had the highest positive correlation, four of them represented the feature with the lowest correlation. With this feature selection method, the number of features decreased from 56 to 10. Secondly, the CfsSubsetEval algorithm selected 29 features to generate a feature subset. The performance results of the ML algorithms according to these two different FS techniques are given in Figure 6. The performance of the models was evaluated for both FS techniques. As seen in Figure 6, it could be observed that the FS with the CfsSubsetEval method gave more  
Figure 5: Comparison of the performance of ML algorithms after applying normalization techniques to categorical variables after 10-fold cross validation: (**a**) R\({}^{2}\), (**b**) RMSE and (**c**) MAE. successful R\({}^{2}\) results than that of the FS with the correlation coefficient method. As the R\({}^{2}\) results were taken into consideration, the KNN algorithm showed the highest improvement by increasing from 0.8143 to 0.8561. Likewise, as shown in the RMSE graph, after the application of the FS with CfsSubsetEval algorithms, a significant decrease in the RMSE value was obtained for the KNN algorithm compared to other algorithms. In general, there was no significant change in the performance of ML models. In general, the fact that ML performances do not change or increase is one of the most important indicators that the application of feature selection method works. Therefore, it could be seen that FS was very effective for this problem. In particular, FS with the CfsSubsetEval method was found to be very efficient.