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Performance comparison of different machine learning methods using RapidMiner
| Model | Accuracy | Standard Deviation | Gains |
|---|---|---|---|
| Naive Bayes | 0.56 | 0.1 | 0.0 |
| Generalized Linear Model | 0.58 | 0.1 | 4.0 |
| Logistic Regression | 0.56 | 0.1 | 0.0 |
| Fast Large Margin | 0.50 | 0.1 | -6.0 |
| Deep Learning | 0.56 | 0.1 | 0.0 |
| Decision Tree | 0.50 | 0.1 | -4.0 |
| Random Forest | 0.60 | 0.1 | 4.0 |
| Gradient Boosted Trees | 0.54 | 0.1 | -4.0 |
| Support Vector Machine | 0.58 | 0.1 | 2.0 |
