machine learning in coding（python）：使用xgboost构建预测模型

最新推荐文章于 2025-06-18 08:10:03 发布

mmc2015

最新推荐文章于 2025-06-18 08:10:03 发布

阅读量7k

点赞数 1

CC 4.0 BY-SA版权

本文链接：https://blog.youkuaiyun.com/mmc2015/article/details/47304779

本文介绍如何在Python环境下，利用机器学习库scikit-learn和强大的XGBoost框架建立预测模型，实现高效的数据预测分析。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

接上篇：http://blog.youkuaiyun.com/mmc2015/article/details/47304591

def xgboost_pred(train,labels,test):
	params = {}
	params["objective"] = "reg:linear"
	params["eta"] = 0.005
	params["min_child_weight"] = 6
	params["subsample"] = 0.7
	params["colsample_bytree"] = 0.7
	params["scale_pos_weight"] = 1
	params["silent"] = 1
	params["max_depth"] = 9
    
    
	plst = list(params.items())

	#Using 5000 rows for early stopping. 
	offset = 4000

	num_rounds = 10000
	xgtest = xgb.DMatrix(test)

	#create a train and validation dmatrices 
	xgtrain = xgb.DMatrix(train[offset:,:], label=labels[offset:])
	xgval = xgb.DMatrix(train[:offset,:], label=labels[:offset])

	#train using early stopping and predict
	watchlist = [(xgtrain, 'train'),(xgval, 'val')]
	model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stop