## 3 Results
### ML Modeling Results  
Since bioinformatical working environments are not standardized, in our opinion, it is essential to test and explore the random forest algorithm in different circumstances. We applied the practical ML modeling utilizing the random forest classifier implementations from two different experimental environments, Python-based Scikit-learn and KNIME. Moreover, we tried different data normalization/scaling techniques, splitting ratio and classifier parameters to provision and maximize models' performance metrics. The process were designed following a two-phase strategy, where the first stage's most significant features were used as a narrowed input scope for the second phase. Created models were additionally analyzed using k-fold cross-validation and hyperparameter tuning techniques. The main idea of this concept was identifying and observing the most significant features resulting from the second phase. Scikit-learn standard scaler and Z-score normalization resulted in considerable research Cronbach's alpha coefficients of over 0.85 for both resistant and not resistant sample groups. Two different random forest classifiers were designed with a cross-validation value of 20% as testing data. The standard scaler classifier performed with overall accuracies of 0.8. The classifier designed with z-score normalization performed with an overall 0.833 accuracy. Created ML models were further analyzed by trying k-fold cross-validation and hyperparameter tuning using the default built-in Randomized-SearchCV/GridSearchCV libraries (tuning the number of estimators, maximum depth of the threes, minimum number of samples required to split an internal node, a minimum number of samples required to be at a leaf node), and different algorithm parameter value setups. Since no significant improvements from the k-fold cross-validation were observed, we continued using the algorithm parameter tuning using different combinations for the number of trees in the forest (n_estimators), maximum depth of the tree (max_depth), and the number of features to consider when looking for the best split (max_features). Thus, the parameters' setup of n_estimators = 55, max_depth = 5 and max_features = 3 resulted in increased algorithm metrics with an overall accuracy of 0.9. KNIME predictor configured \begin{table}
\begin{tabular}{c c} \hline \hline
**ML Algorithms** & **Overall Accuracy *** \\ \hline Naive Bayes & 0.429 \\ Logistic Regression & 0.425 \\ K-Nearest Neighbors & 0.325 \\ Support Vector Machine & 0.497 \\ Decision Tree & 0.764 \\ \hline \hline \end{tabular}  
* The overall algorithm accuracy was selected as the main algorithm selection indicator.  
\end{table} 
Table 1: Screening modeling phase algorithms overall accuracies. the cross-validation value of 25% test data using the stratified sampling by additionally introduced'resistance' target feature, was designed with z-score data normalization, where a Cronbach's alpha of 0.854 was calculated. The Tree Ensemble Learner was configured with the Gini index split criterion. Then, we proceed with the second ML iteration using the reduced most important features set. The standard scaled classifier (configured with the cross-validation value of 25% test data), where [PERSON]'s alpha coefficient of 0.795 performed with an overall accuracy of 0.917. Experiencing similar behavior to the first iteration, we configured the same parameters set using the following values for the n_estimators = 25, max_depth = 4, and max_features = 3. Area under the curve (AUC) was calculated as value of 0.91. On the other hand, the Tree Ensemble Learner, configured with the Gini index split criterion and cross-validation value of 20% test data using randomly based sampling, resulted in [PERSON]'s alpha coefficient of 0.795 and overall performance accuracy of 0.9. The general performance metrics are available in Tables 2 and 3. We concluded that the tree-based algorithms accomplished the highest scores compared with the other techniques we applied according to the performance metrics.