机器学习笔记五：使用决策树实现鸢尾花分类

一、什么是决策树？

决策树(Decision Tree)：通俗易懂之介绍

二、DecisionTreeClassifier类参数介绍

sklearn.tree.DecisionTreeClassifier( criterion='gini', 
									 splitter='best', 
									 max_depth=None, 
									 min_samples_split=2, 
									 min_samples_leaf=1, 
									 min_weight_fraction_leaf=0.0, 
									 max_features=None, 
									 random_state=None, 
									 max_leaf_nodes=None, 
									 min_impurity_decrease=0.0,
									 min_impurity_split=None, 
									 class_weight=None, 
									 presort='deprecated', 
									 ccp_alpha=0.0)

三、鸢尾花分类案例

1、导入相关模块

import sklearn
from sklearn import datasets,tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

2、加载并划分数据

iris = datasets.load_iris()
iris_feature = iris.data
iris_target = iris.target

feature_train, feature_test, target_train, target_test = 
			train_test_split(iris_feature, iris_target, test_size=0.33,
                                                                          random_state=0)

3、建立决策树模型、训练预测以及计算准确度

#'gini'对应CART算法，'entropy'对应ID3算法
dt_model = tree.DecisionTreeClassifier(criterion='gini', min_samples_leaf=3)
dt_model.fit(feature_train, target_train)
predict_results = dt_model.predict(feature_test)

print (predict_results)
print (target_test)

print (accuracy_score(predict_results, target_test))

[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
 2 1 1 2 0 2 0 0 1 2 2 2 2]
[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
 1 1 1 2 0 2 0 0 1 2 2 2 2]
0.98

4、绘制决策树

%matplotlib inline
fig=plt.figure(figsize=(10,10))
tree.plot_tree(dt_model,filled="True",
               feature_names=["Sepal length","Sepal width",
               				  "Petal length","Petal width"],
               class_names=["setosa","versicolor","virginica"])

参考链接：机器学习——决策树，DecisionTreeClassifier参数详解，决策树可视化查看树结构