xgboost如何画决策树

暂时还没有搞清楚xgboost中每一个树的权重是怎么样的，以及每个树的结果和最终的结果之间的关系是怎么样的？后面再补上，

下面如何xgboost中的决策树

# -*- coding: utf-8 -*-
"""
Created on Tue Mar  9 16:16:56 2021

@author: Administrator
"""

#%%导入模块
import pandas as pd 
import numpy as np
from scipy import stats
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
plt.rc("font",family="SimHei",size="12")  #解决中文无法显示的问题


#%%导入数据
creditcard = pd.read_csv('D:/信用卡欺诈检测/creditcard.csv/creditcard.csv')
creditcard.info()

import xgboost as xgb
from xgboost import XGBClassifier 
from xgboost import plot_tree
import matplotlib.pyplot as plt

X = creditcard.iloc[:,0:-1]
y = creditcard.Class
model = XGBClassifier(max_depth=4, n_estimators=200, learn_rate=0.1)
model.fit(X, y)


def ceate_feature_map(features):
    outfile = open('xgb.fmap', 'w')
    i = 0
    for feat in features:
        outfile.write('{0}\t{1}\tq\n'.format(i, feat))
        i = i + 1
    outfile.close()
'''
X_train.columns在第一段代码中也已经设置过了。
特别需要注意：列名字中不能有空格。
'''
ceate_feature_map(X.columns)

plot_tree(model, num_trees=199, fmap='xgb.fmap')
fig = plt.gcf()
fig.set_size_inches(150, 100)
#plt.show()
fig.savefig('tree.png')

 

 下面简单介绍一下plot_tree参数