scikit-learn机器学习(四)使用决策树做分类,并画出决策树,随机森林对比

数据来自 UCI 数据集 匹马印第安人糖尿病数据集

载入数据

# -*- coding: utf-8 -*-

import pandas as pd

import matplotlib

matplotlib.rcParams['font.sans-serif']=[u'simHei']

matplotlib.rcParams['axes.unicode_minus']=False

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report

from sklearn.pipeline import Pipeline

from sklearn.model_selection import GridSearchCV

from sklearn.datasets import load_breast_cancer

data_set = pd.read_csv('pima-indians-diabetes.csv')

data = data_set.values[:,:]

y = data[:,8]

X = data[:,:8]

X_train,X_test,y_train,y_test = train_test_split(X,y)

建立决策树，网格搜索微调模型

# In[1] 网格搜索微调模型

pipeline = Pipeline([

        ('clf',DecisionTreeClassifier(criterion='entropy'))

        ])

parameters={

        'clf__max_depth':(3,5,10,15,20,25,30,35,40),

        'clf__min_samples_split':(2,3),

        'clf__min_samples_leaf':(1,2,3)

        }

#GridSearchCV 用于系统地遍历多种参数组合，通过交叉验证确定最佳效果参数。

grid_search = GridSearchCV(pipeline,parameters,n_jobs=-1,verbose=-1,scoring='f1')

grid_search.fit(X_train,y_train)

# 获取搜索到的最优参数

best_parameters = grid_search.best_estimator_.get_params()

print("最好的F1值为：",grid_search.best_score_)

print('最好的参数为：')

for param_name in sorted(parameters.keys()):

    print('t%s: %r' % (param_name,best_parameters[param_name]))

# In[2] 输出预测结果并评价

predictions = grid_search.predict(X_test)

print(classification_report(y_test,predictions))

最好的F1值为： 0.5573515325670498

最好的参数为：

tclf__max_depth: 5

tclf__min_samples_leaf: 1

tclf__min_samples_split: 2

评价模型

# In[2] 输出预测结果并评价

predictions = grid_search.predict(X_test)

print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

         0.0       0.74      0.89      0.81       124

         1.0       0.67      0.43      0.52        68

画出决策树

# In[3]打印树

from sklearn import tree

feature_name=data_set.columns.values.tolist()[:-1]   # 列名称

DT = tree.DecisionTreeClassifier(criterion='entropy',max_depth=5,min_samples_split=2,min_samples_leaf=5)

DT.fit(X_train,y_train)

'''

# 法一

import pydotplus

from sklearn.externals.six import StringIO

dot_data = StringIO()

tree.export_graphviz(DT,out_file = dot_data,feature_names=feature_name,

                     class_names=["有糖尿病","无病"],filled=True,rounded=True,

                     special_characters=True)

graph = pydotplus.graph_from_dot_data(dot_data.getvalue())

graph.write_pdf("Tree.pdf")

print('Visible tree plot saved as pdf.')

'''

# 法二

import graphviz

#ID3为决策树分类器fit之后得到的模型，注意这里必须在fit后执行，在predict之后运行会报错

dot_data = tree.export_graphviz(DT, out_file=None,feature_names=feature_name,class_names=["有糖尿病","无病"]) # doctest: +SKIP

graph = graphviz.Source(dot_data) # doctest: +SKIP

#在同级目录下生成tree.pdf文件

graph.render("tree2") # doctest: +SKIP

随机森林

# -*- coding: utf-8 -*-

import pandas as pd

import matplotlib

matplotlib.rcParams['font.sans-serif']=[u'simHei']

matplotlib.rcParams['axes.unicode_minus']=False

from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report

from sklearn.pipeline import Pipeline

from sklearn.model_selection import GridSearchCV

from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import load_breast_cancer

data_set = pd.read_csv('pima-indians-diabetes.csv')

data = data_set.values[:,:]

y = data[:,8]

X = data[:,:8]

X_train,X_test,y_train,y_test = train_test_split(X,y)

RF = RandomForestClassifier(n_estimators=10,random_state=11)

RF.fit(X_train,y_train)

predictions = RF.predict(X_test)

print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

         0.0       0.82      0.91      0.86       126

         1.0       0.78      0.61      0.68        66

   micro avg       0.81      0.81      0.81       192

   macro avg       0.80      0.76      0.77       192

weighted avg       0.80      0.81      0.80       192