吴裕雄 python 机器学习——分类决策树模型
import numpy as np
import matplotlib.pyplot as plt from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor def load_data():
'''
加载用于分类问题的数据集。数据集采用 scikit-learn 自带的 iris 数据集
'''
# scikit-learn 自带的 iris 数据集
iris=datasets.load_iris()
X_train=iris.data
y_train=iris.target
return train_test_split(X_train, y_train,test_size=0.25,random_state=0,stratify=y_train) #分类决策树DecisionTreeClassifier模型
def test_DecisionTreeClassifier(*data):
X_train,X_test,y_train,y_test=data
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)
print("Training score:%f"%(clf.score(X_train,y_train)))
print("Testing score:%f"%(clf.score(X_test,y_test))) # 产生用于分类问题的数据集
X_train,X_test,y_train,y_test=load_data()
# 调用 test_DecisionTreeClassifier
test_DecisionTreeClassifier(X_train,X_test,y_train,y_test)
def test_DecisionTreeClassifier_criterion(*data):
'''
测试 DecisionTreeClassifier 的预测性能随 criterion 参数的影响
'''
X_train,X_test,y_train,y_test=data
criterions=['gini','entropy']
for criterion in criterions:
clf = DecisionTreeClassifier(criterion=criterion)
clf.fit(X_train, y_train)
print("criterion:%s"%criterion)
print("Training score:%f"%(clf.score(X_train,y_train)))
print("Testing score:%f"%(clf.score(X_test,y_test))) # 调用 test_DecisionTreeClassifier_criterion
test_DecisionTreeClassifier_criterion(X_train,X_test,y_train,y_test)
def test_DecisionTreeClassifier_splitter(*data):
'''
测试 DecisionTreeClassifier 的预测性能随划分类型的影响
'''
X_train,X_test,y_train,y_test=data
splitters=['best','random']
for splitter in splitters:
clf = DecisionTreeClassifier(splitter=splitter)
clf.fit(X_train, y_train)
print("splitter:%s"%splitter)
print("Training score:%f"%(clf.score(X_train,y_train)))
print("Testing score:%f"%(clf.score(X_test,y_test))) # 调用 test_DecisionTreeClassifier_splitter
test_DecisionTreeClassifier_splitter(X_train,X_test,y_train,y_test)
def test_DecisionTreeClassifier_depth(*data,maxdepth):
'''
测试 DecisionTreeClassifier 的预测性能随 max_depth 参数的影响
'''
X_train,X_test,y_train,y_test=data
depths=np.arange(1,maxdepth)
training_scores=[]
testing_scores=[]
for depth in depths:
clf = DecisionTreeClassifier(max_depth=depth)
clf.fit(X_train, y_train)
training_scores.append(clf.score(X_train,y_train))
testing_scores.append(clf.score(X_test,y_test)) ## 绘图
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(depths,training_scores,label="traing score",marker='o')
ax.plot(depths,testing_scores,label="testing score",marker='*')
ax.set_xlabel("maxdepth")
ax.set_ylabel("score")
ax.set_title("Decision Tree Classification")
ax.legend(framealpha=0.5,loc='best')
plt.show() # 调用 test_DecisionTreeClassifier_depth
test_DecisionTreeClassifier_depth(X_train,X_test,y_train,y_test,maxdepth=100)
import os
import pydotplus from io import StringIO
from sklearn.tree import export_graphviz
from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor X_train,X_test,y_train,y_test=load_data()
clf = DecisionTreeClassifier()
clf.fit(X_train,y_train)
export_graphviz(clf,"F://out")
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