From: Optimizing classification efficiency with machine learning techniques for pattern matching
Algorithms | Accuracy | Precision | ROC_AUC | Recall | F1 score | Execution time | |
---|---|---|---|---|---|---|---|
1 | KNN | 0.778 | 0.62 | 0.701 | 0.65 | 0.79 | 14.448 |
2 | Decision Tree | 0.815 | 0.92 | 0.891 | 0.71 | 0.8 | 13.271 |
3 | Random Forest | 0.609 | 0.67 | 0.623 | 0.47 | 0.55 | 12.983 |
4 | Naive Bayes | 0.838 | 0.83 | 0.855 | 0.88 | 0.94 | 12.606 |
5 | SVM RBF | 0.925 | 0.83 | 0.937 | 0.88 | 0.94 | 13.173 |
6 | SVM Sigmoid | 0.925 | 0.83 | 0.952 | 0.88 | 0.94 | 14.189 |
7 | SVM Linear | 0.963 | 0.91 | 0.963 | 0.94 | 0.97 | 10.059 |