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The accuracy of the model on dataset used for constructing the model (training accuracy) and the accuracy of the samples on the examples the model has not seen (test accuracy)
| Classifer | Training accuracy | Test accuracy |
|---|---|---|
| XgBoost | 0.875 | 0.852 |
| Random Forest | 1.000 | 0.857 |
| Neural network | 0.635 | 0.696 |
| SVM | 0.597 | 0.564 |
Significantly lower accuracy in the training set implies overfitting.
