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1.10 Comparison of Numerical Interpretation
ОглавлениеA summarized version of the AUC results of the above discussed supervised learning methods is given below in Table 1.9 as a comparison of methods. The result indicates that the performance of Random Forest, K-Nearest Neighbor, Decision Tree, and Support Vector Classifier performs outstandingly in both Train and test data sets. Whereas the Logistic Regression and Neural Network perform Outstanding on the testing data set only. It indicates that the models used in Logistic Regression and Neural Network need improvement in the training data set. Hence, the accuracy level will be achieved.
Table 1.9 AUC: Comparison of numerical interpretations.
| S. No. | Supervised Learning Parameter | AUC Training Data Value (T1) | AUC Test Data Value (T2) | Result |
| 1 | Logistic Regression | 0.8374022 | 0.9409523 | T1: Excellent T2: Outstanding |
| 2 | Random Forest | 1.0000000 | 1.0000000 | T1: Outstanding T2: Outstanding |
| 3 | K-Nearest Neighbor | 1.0000000 | 1.0000000 | T1: Outstanding T2: Outstanding |
| 4 | Decision Tree | 0.9588996 | 0.9773333 | T1: OutstandingT2: Outstanding |
| 5 | Support Vector Classifier | 1.0000000 | 0.9773333 | T1: Outstanding T2: Outstanding |
| 6 | Neural Networks | 0.8366730 | 0.9415238 | T1: Excellent T2: Outstanding |
| Index: 0.5: No Discriminant, 0.6–0.8: Can be considered accepted, 0.8–0.9: Excellent, >0.9: Outstanding |
