Nikolay-Lysenko
/ xgb_quantile_loss.py
Last active
October 25, 2023 13:26
Customized loss function for quantile regression with XGBoost
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| import numpy as np | |
| def xgb_quantile_eval(preds, dmatrix, quantile=0.2): | |
| """ | |
| Customized evaluational metric that equals | |
| to quantile regression loss (also known as | |
| pinball loss). | |
| Quantile regression is regression that |
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| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |