## 3 Results
### Novel Tank Test
The accuracy of the Knn classifier on test data is 0.73. [[0 7]  
[1 22]]  
[0 1]  
0.4782608695652174  
The accuracy of the XGBoost classifier on training data is 0.94. The accuracy of the XGBoost classifier on test data is 0.57. [[0 7]  
[0 1]  
0.3695652173913043  
The accuracy of the Decision Tree classifier on training data is 0.99. The accuracy of the Decision Tree classifier on test data is 0.57. [[1 6]  
[7 16]]  
[0 1]  
0.4192546583850931  
The accuracy of the Random Forest classifier on training data is 0.97. The accuracy of the Random Forest classifier on test data is 0.73. [[1 6][2 21]]  
[0 1]  
0.5279503105590062  
Significantly better results of the accuracy of the classification algorithms were recorded on the reduced data set. SVM records the lowest overfitting and a relatively acceptable accuracy, but erroneously predicts the existence of only one class, the majority one, which shows that the algorithm is sensitive (gives erroneous results) to unbalanced classes. The Random Forest algorithm gave the best results (as expected). Decision Tree obtained a very high classification accuracy, only with a high overfitting rate and a low AUC value. It should be noted that the confusion matrix has the most errors in the case of the minority class, so we will be forced to manage this situation (the presence of unbalanced classes) in order to obtain correct results. For the NTT test, the lowest overfitting was recorded for KNN, and the highest accuracy values were recorded for the XGBoost, Decision Tree, and Random Forest algorithms run on the drive data, only with higher overfitting values. NTT ML  
OUT: There are 42 samples in the training set and 18 samples in the test set. OUT: The accuracy of the SVM classifier on training data is 0.62. The accuracy of the SVM classifier on test data is 0.11. Significantly better accuracy results were also recorded on the reduced data set in the case of the NTT test. SVM and KNN recorded the lowest overfitting and a fairly high accuracy, as well as the AUC value. In this case, the Random Forest and Decision Tree algorithms gave good results in accuracy, overfitting, and AUC. Overall, however, XGBoost held supremacy in all classification tests: accuracy, overfitting rate, and AUC value. It should be noted that the confusion matrix presented the most errors also for the minority class, only in this case with much lower values. NTT ML-reduced  
There are 41 samples in the training set and 18 samples in the test set. The accuracy of the SVM classifier on training data is 0.90. The accuracy of the SVM classifier on test data is 0.89. [[2 2]  
[0 14]]  
[0 1]  
0.75  
The accuracy of the Knn classifier on training data is 0.88. The accuracy of the Knn classifier on test data is 0.89. [[2 2]  
[0 14]]  
[0 1]  
0.75  
The accuracy of the XGBoost classifier on training data is 0.95. The accuracy of the XGBoost classifier on test data is 0.89. [[2 2]  
[0 14]]  
[0 1]  
0.75  
The accuracy of the Decision Tree classifier on training data is 1.00. The accuracy of the Decision Tree classifier on test data is 0.67. [[2 2]  
[4 10]]  
[0 1]  
0.6071428571428572  
The accuracy of the Random Forest classifier on training data is 1.00. The accuracy of the Random Forest classifier on test data is 0.78. [[1 3]  
[1 13]]  
[0 1]  
0.5892857142857143  
Next, we used PCA in Python to reduce the size to 2 or 3, so that we can browse and better understand the data. Using the variance explained ratio attribute to reduce the size of the data in the MAZE test, the first main component contained 67.96% of the variance and the second main component 26.25% of the variance, together, the two components containing 94.21% of the information. We can also notice from the graph that the classes are not well separated from each other. The same can be seen from the graph represented for the case of reduced data (Figures 11 and 12). Using the same program, we can see in the case of the NTT test the first main component contains 43.44% of the variance and the second main component contains 26.79% of the variance.