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Predictive Uncertainty in Gradient-Boosted Regression Trees : A Muon Energy Reconstruction Case Study

This repository hosts the code base for the report that I wrote during my internship at INPP, at NCSR Demokritos with Evangelia Drakopoulou at the ANNIE experiment of Fermilab.

To create a conda environment with the needed dependecies run: conda env create -f env.yml Then: conda activate reco_env and pip install ibug

To reproduce table 1 and train uncertainty models for tables 2 and 3 run bash reproduce_train.sh. The results for the table 1 will be in the 'results' directory.

To reproduce the metrics of each uncertainty model run one-by-one: python ibug_catb_test.py python ibug_xgb_test.py python CBU_pred.py python ibug_cbu.py

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