机器学习之监督学习--（分类）决策树②

注：数据集在文章末尾

（1）决策树–线性二分类

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report
from sklearn import tree

# 载入数据
data = np.genfromtxt("LR-testSet.csv", delimiter=",")
x_data = data[:,:-1]
y_data = data[:,-1]

plt.scatter(x_data[:,0],x_data[:,1],c=y_data) 
plt.show()

输出：

# 创建决策树模型
model = tree.DecisionTreeClassifier()
# 输入数据建立模型
model.fit(x_data, y_data)

# 导出决策树
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['x','y'],
                                class_names = ['label0','label1'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)

# 获取数据值所在的范围
x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1

# 生成网格矩阵
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))

z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据
z = z.reshape(xx.shape)
# 等高线图
cs = plt.contourf(xx, yy, z)
# 样本散点图
plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
plt.show()

输出：

# 预测
predictions = model.predict(x_data)
print(classification_report(predictions,y_data))

输出：

（2）决策树–非线性二分类

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report
from sklearn import tree
from sklearn.model_selection import train_test_split

# 载入数据
data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
x_data = data[:,:-1]
y_data = data[:,-1]
    
plt.scatter(x_data[:,0],x_data[:,1],c=y_data) 
plt.show()

输出：

#分割数据
x_train,x_test,y_train,y_test = train_test_split(x_data, y_data) 

# 创建决策树模型
# max_depth，树的深度
# min_samples_split 内部节点再划分所需最小样本数
model = tree.DecisionTreeClassifier(max_depth=7,min_samples_split=4)
# 输入数据建立模型
model.fit(x_train, y_train)

# 导出决策树
import graphviz # http://www.graphviz.org/

dot_data = tree.export_graphviz(model, 
                                out_file = None, 
                                feature_names = ['x','y'],
                                class_names = ['label0','label1'],
                                filled = True,
                                rounded = True,
                                special_characters = True)
graph = graphviz.Source(dot_data)

# 获取数据值所在的范围
x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1

# 生成网格矩阵
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
                     np.arange(y_min, y_max, 0.02))

z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据
z = z.reshape(xx.shape)
# 等高线图
cs = plt.contourf(xx, yy, z)
# 样本散点图
plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)
plt.show()

# 训练集预测
predictions = model.predict(x_train)
print(classification_report(predictions,y_train))

# 测试集预测
predictions = model.predict(x_test)
print(classification_report(predictions,y_test))

数据集：“LR-testSet.csv”：

-0.017612,14.053064,0
-1.395634,4.662541,1
-0.752157,6.53862,0
-1.322371,7.152853,0
0.423363,11.054677,0
0.406704,7.067335,1
0.667394,12.741452,0
-2.46015,6.866805,1
0.569411,9.548755,0
-0.026632,10.427743,0
0.850433,6.920334,1
1.347183,13.1755,0
1.176813,3.16702,1
-1.781871,9.097953,0
-0.566606,5.749003,1
0.931635,1.589505,1
-0.024205,6.151823,1
-0.036453,2.690988,1
-0.196949,0.444165,1
1.014459,5.754399,1
1.985298,3.230619,1
-1.693453,-0.55754,1
-0.576525,11.778922,0
-0.346811,-1.67873,1
-2.124484,2.672471,1
1.217916,9.597015,0
-0.733928,9.098687,0
-3.642001,-1.618087,1
0.315985,3.523953,1
1.416614,9.619232,0
-0.386323,3.989286,1
0.556921,8.294984,1
1.224863,11.58736,0
-1.347803,-2.406051,1
1.196604,4.951851,1
0.275221,9.543647,0
0.470575,9.332488,0
-1.889567,9.542662,0
-1.527893,12.150579,0
-1.185247,11.309318,0
-0.445678,3.297303,1
1.042222,6.105155,1
-0.618787,10.320986,0
1.152083,0.548467,1
0.828534,2.676045,1
-1.237728,10.549033,0
-0.683565,-2.166125,1
0.229456,5.921938,1
-0.959885,11.555336,0
0.492911,10.993324,0
0.184992,8.721488,0
-0.355715,10.325976,0
-0.397822,8.058397,0
0.824839,13.730343,0
1.507278,5.027866,1
0.099671,6.835839,1
-0.344008,10.717485,0
1.785928,7.718645,1
-0.918801,11.560217,0
-0.364009,4.7473,1
-0.841722,4.119083,1
0.490426,1.960539,1
-0.007194,9.075792,0
0.356107,12.447863,0
0.342578,12.281162,0
-0.810823,-1.466018,1
2.530777,6.476801,1
1.296683,11.607559,0
0.475487,12.040035,0
-0.783277,11.009725,0
0.074798,11.02365,0
-1.337472,0.468339,1
-0.102781,13.763651,0
-0.147324,2.874846,1
0.518389,9.887035,0
1.015399,7.571882,0
-1.658086,-0.027255,1
1.319944,2.171228,1
2.056216,5.019981,1
-0.851633,4.375691,1
-1.510047,6.061992,0
-1.076637,-3.181888,1
1.821096,10.28399,0
3.01015,8.401766,1
-1.099458,1.688274,1
-0.834872,-1.733869,1
-0.846637,3.849075,1
1.400102,12.628781,0
1.752842,5.468166,1
0.078557,0.059736,1
0.089392,-0.7153,1
1.825662,12.693808,0
0.197445,9.744638,0
0.126117,0.922311,1
-0.679797,1.22053,1
0.677983,2.556666,1
0.761349,10.693862,0
-2.168791,0.143632,1
1.38861,9.341997,0
0.317029,14.739025,0

数据集：“LR-testSet.2csv”：

0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
0.062788,-0.16301,1
0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
0.322,0.69225,1
0.13767,0.57529,1
-0.0063364,0.39985,1
-0.092742,0.55336,1
-0.20795,0.35599,1
-0.20795,0.17325,1
-0.43836,0.21711,1
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0.18376,0.93348,0
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