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@@ -64,3 +64,9 @@ configs:
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  - split: test
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  path: data/test-*
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+ Feature Engineered version of [this](https://huggingface.co/datasets/uncledecart/fed-turbofan) dataset. Key differences:
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+ - GBDT feature selection (selected on train data, target is rul, threshold is 0.01, lightgbm.LGBMRegressor is used)
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+ - Sensor-wise z_score and min_max normalisation (effectively just min_max, applied z_score as in [this](https://www.researchgate.net/publication/355164116_Data-Driven_Deep_Learning-Based_Attention_Mechanism_for_Remaining_Useful_Life_Prediction_Case_Study_Application_to_Turbofan_Engine_Analysis) paper)