xgboost介绍

2019-11-11 06:08:55

字体：大中小

来源：转载

供稿：网友

搞了一下午，终于把xgboost安上了，头晕眼花，下面总结一下安装过程和xgboost的原理因为不想编译，在CSDN上找到编译后的xgboost包网址为http://download.csdn.net/detail/onepiecehuiyu/9518553 在命令行中(win+R)cmd中进入”…/xgboost-master/python-package/” 然后输入”python setup.py install 解压在”…/Lib/site-packages/xgboost-0.4-py3.5.egg”文件下的xgboost文件复制到”…/Lib/site-packages”下。 import xgboost as xgb无错误，说明安装成功测试数据结果为[ 0.03688221]，性能在随机森林和ExtraTrees之间

原理：

这里写图片描述

测试代码

# -*- coding: utf-8 -*-"""Created on Mon Feb 6 15:08:02 2017@author: Administrator"""import xgboost as xgbimport DTimport numpy as npTestSetN = 500dataset = DT.LoadData()y = []X = []for data in dataset: y += [data[-1]] X += [data[:-1]]dtrain = xgb.DMatrix(X[:-TestSetN], y[:-TestSetN])dtest = xgb.DMatrix(X[-1])evallist = [(dtest,'eval'), (dtrain,'train')]param = {'bst:max_depth':2, 'bst:eta':1, 'silent':1, 'objective':'reg:linear' }param['nthread'] = 4plst = param.items()num_round = 10bst = xgb.train(plst, dtrain)dtest = xgb.DMatrix(X[-TestSetN:-1] + [X[-1]])PRint((sum(np.transpose((bst.predict(dtest) - [y[-TestSetN:-1] + [y[-1]]])**2)/TestSetN)**0.5))

上一篇：substring用法总结

下一篇：算法Day12-层次遍历二叉树