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Comparison of Measures for ML techniques
| Algorithm | Training Accuracy |
Testing Accuracy |
Miss Classification Rate | Run Time (Seconds) |
|---|---|---|---|---|
| K-Nearest Neighbour | 81.86 % | 77.06 % | 0.229 % | 1.7730 |
| Logistic Regression | 90.88 % | 90.20 % | 0.097 % | 3.4950 |
| Support Vector Machine | 90.88 % | 90.20 % | 0.097 % | 0.1274 |
| Naive Bayes | 89.68 % | 90.72 % | 0.092 % | 0.1034 |
| Decision Tree | 94.23 % | 92.52 % | 0.074 % | 0.0056 |
| Random Forest | 95.35 % | 95.36 % | 0.046 % | 1.2365 |
| Gradient Boosting | 97.24 % | 96.90 % | 0.030 % | 2.1504 |
