Boosted Decision Tree Classifier for Muons in 16789 CC and NC Events
Utilizing a Boosted Decision Tree (BDT) classifier to distinguish muon hits from other hits in 16789 CC and NC neutrino events. The model was trained and evaluated using 10% of data, achieving an ROC integral of 0.991. The classifier utilizes variables like time residuals, distance from muon track, and reconstructed shower energy. Different classification thresholds impact results, as shown in confusion matrices. Visualizations include Receiver Operator Characteristic (ROC) plots showcasing True Positive Rate and False Positive Rate.
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