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Arman Ulusoy 2024-06-12 16:03:38 +02:00
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@ -202,6 +202,10 @@ The detection ability of the NeuroKit2 library is tested to detect features in t
The exact process can be found in the notebook: [features_detection.ipynb](notebooks/features_detection.ipynb).
### ML-models
First, the grid was tested to find the best model which was then trained to identify the best hyperparameters out of it. That way, an accuracy of 83 % was achieved with the XGBoost classifier. The Gradient Boosting Tree Classifier had an accuracy of 82%.
<br>The detailed procedures can be found in the following notebooks:
<br>[ml_xgboost.ipynb](notebooks/ml_xgboost.ipynb)
<br>[ml_grad_boost_tree.ipynb](notebooks/ml_grad_boost_tree.ipynb)
## Contributing