机器学习之决策树----python实现

最新推荐文章于 2023-08-07 07:30:00 发布

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最新推荐文章于 2023-08-07 07:30:00 发布

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CC 4.0 BY-SA版权

分类专栏：机器学习类

本文链接：https://blog.youkuaiyun.com/super_he_pi/article/details/83309385

本文介绍了使用Python进行决策树机器学习的实践过程，通过训练数据集(train_set_model)建立模型，并在测试数据集(test_results)上验证效果。

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# -*- coding: utf-8 -*-
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import classification_report
from sklearn.cross_validation import train_test_split 
plt.switch_backend('agg') 

# 数据读入 
data = []
labels = []
with open("file_fac_abe.txt") as ifile:
	for line in ifile:
		tokens = line.strip().split(',')
		#print('tokens:',tokens)
		data.append([int(tk) for tk in tokens[:-1]])
		labels.append(tokens[-1])

x = np.array(data)
labels = np.array(labels)
y = np.zeros(labels.shape)
print('x:',x)
print(len(x))

print('labels:',labels)
print(len(labels))

print('y:',y)
print(len(y))
 
#标签转换为0/1
y[labels=='LCS']=1
print('y:',y)
 
#拆分训练数据与测试数据
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2,random_state=0)

'''
print('x_train:',x_train)
print(len(x_train))
print('x_test:',x_test)
print(len(x_test))
print