xgboost介紹

2019-11-11 04:43:34

字體：大中小

來源：轉載

供稿：網友

搞了一下午，終于把xgboost安上了，頭暈眼花，下面總結一下安裝過程和xgboost的原理因為不想編譯，在CSDN上找到編譯后的xgboost包網址為http://download.csdn.net/detail/onepiecehuiyu/9518553 在命令行中(win+R)cmd中進入”…/xgboost-master/python-package/” 然后輸入”python setup.py install 解壓在”…/Lib/site-packages/xgboost-0.4-py3.5.egg”文件下的xgboost文件復制到”…/Lib/site-packages”下。 import xgboost as xgb無錯誤，說明安裝成功測試數據結果為[ 0.03688221]，性能在隨機森林和ExtraTrees之間

原理：

這里寫圖片描述

測試代碼

# -*- 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))

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