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wuyunfa / XGBOOSTQUANTILE.py

Created January 5, 2023 14:14 — forked from benoitdescamps/XGBOOSTQUANTILE.py

	class XGBQuantile(XGBRegressor):
	def __init__(self,quant_alpha=0.95,quant_delta = 1.0,quant_thres=1.0,quant_var =1.0,base_score=0.5, booster='gbtree', colsample_bylevel=1,
	colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
	n_jobs=1, nthread=None, objective='reg:linear', random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,silent=True, subsample=1):
	self.quant_alpha = quant_alpha
	self.quant_delta = quant_delta
	self.quant_thres = quant_thres
	self.quant_var = quant_var

	super().__init__(base_score=base_score, booster=booster, colsample_bylevel=colsample_bylevel,

benoitdescamps / XGBOOSTQUANTILE.py

Last active July 12, 2023 04:58

	class XGBQuantile(XGBRegressor):
	def __init__(self,quant_alpha=0.95,quant_delta = 1.0,quant_thres=1.0,quant_var =1.0,base_score=0.5, booster='gbtree', colsample_bylevel=1,
	colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
	n_jobs=1, nthread=None, objective='reg:linear', random_state=0,reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,silent=True, subsample=1):
	self.quant_alpha = quant_alpha
	self.quant_delta = quant_delta
	self.quant_thres = quant_thres
	self.quant_var = quant_var

	super().__init__(base_score=base_score, booster=booster, colsample_bylevel=colsample_bylevel,

Nikolay-Lysenko / xgb_quantile_loss.py

Last active October 25, 2023 13:26

Customized loss function for quantile regression with XGBoost

	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