鸢尾花分类实践：决策树模型解析

构建一个决策树分类模型，实现对鸢尾花的分类

1.lris数据集介绍：

鸢尾花数据集是机器学习领域中非常经典的一个分类数据集。数据集全名为：Iris Data Set，总共包含150行数据。

每一行由4个特征值及一个目标值（类别变量）组成。

其中4个特征值分别是：萼片长度、萼片宽度、花瓣长度、花瓣宽度

目标值为3种不同类别的鸢尾花：山鸢尾、变色鸢尾、维吉尼亚鸢尾

2.读取数据

Iris数据集里是一个矩阵，每一列代表了萼片或花瓣的长宽，一共4列，每一列代表某个被测量的鸢尾植物，一共采样了150条记录。

from sklearn.datasets import load_iris

iris = load_iris()

iris_feature = iris.data

iris_target = iris.target

print (iris.data)

print (iris.target)

print (len(iris.target) )

print (iris.data.shape )

[[5.1 3.5 1.4 0.2]

[4.9 3. 1.4 0.2]

[4.7 3.2 1.3 0.2]

[4.6 3.1 1.5 0.2]

[5. 3.6 1.4 0.2]

[5.4 3.9 1.7 0.4]

[4.6 3.4 1.4 0.3]

[5. 3.4 1.5 0.2]

[4.4 2.9 1.4 0.2]

[4.9 3.1 1.5 0.1]

[5.4 3.7 1.5 0.2]

[4.8 3.4 1.6 0.2]

[4.8 3. 1.4 0.1]

[4.3 3. 1.1 0.1]

[5.8 4. 1.2 0.2]

[5.7 4.4 1.5 0.4]

[5.4 3.9 1.3 0.4]

[5.1 3.5 1.4 0.3]

[5.7 3.8 1.7 0.3]

[5.1 3.8 1.5 0.3]

[5.4 3.4 1.7 0.2]

[5.1 3.7 1.5 0.4]

[4.6 3.6 1. 0.2]

[5.1 3.3 1.7 0.5]

[4.8 3.4 1.9 0.2]

[5. 3. 1.6 0.2]

[5. 3.4 1.6 0.4]

[5.2 3.5 1.5 0.2]

[5.2 3.4 1.4 0.2]

[4.7 3.2 1.6 0.2]

[4.8 3.1 1.6 0.2]

[5.4 3.4 1.5 0.4]

[5.2 4.1 1.5 0.1]

[5.5 4.2 1.4 0.2]

[4.9 3.1 1.5 0.2]

[5. 3.2 1.2 0.2]

[5.5 3.5 1.3 0.2]

[4.9 3.6 1.4 0.1]

[4.4 3. 1.3 0.2]

[5.1 3.4 1.5 0.2]

[5. 3.5 1.3 0.3]

[4.5 2.3 1.3 0.3]

[4.4 3.2 1.3 0.2]

[5. 3.5 1.6 0.6]

[5.1 3.8 1.9 0.4]

[4.8 3. 1.4 0.3]

[5.1 3.8 1.6 0.2]

[4.6 3.2 1.4 0.2]

[5.3 3.7 1.5 0.2]

[5. 3.3 1.4 0.2]

[7. 3.2 4.7 1.4]

[6.4 3.2 4.5 1.5]

[6.9 3.1 4.9 1.5]

[5.5 2.3 4. 1.3]

[6.5 2.8 4.6 1.5]

[5.7 2.8 4.5 1.3]

[6.3 3.3 4.7 1.6]

[4.9 2.4 3.3 1. ]

[6.6 2.9 4.6 1.3]

[5.2 2.7 3.9 1.4]

[5. 2. 3.5 1. ]

[5.9 3. 4.2 1.5]

[6. 2.2 4. 1. ]

[6.1 2.9 4.7 1.4]

[5.6 2.9 3.6 1.3]

[6.7 3.1 4.4 1.4]

[5.6 3. 4.5 1.5]

[5.8 2.7 4.1 1. ]

[6.2 2.2 4.5 1.5]

[5.6 2.5 3.9 1.1]

[5.9 3.2 4.8 1.8]

[6.1 2.8 4. 1.3]

[6.3 2.5 4.9 1.5]

[6.1 2.8 4.7 1.2]

[6.4 2.9 4.3 1.3]

[6.6 3. 4.4 1.4]

[6.8 2.8 4.8 1.4]

[6.7 3. 5. 1.7]

[6. 2.9 4.5 1.5]

[5.7 2.6 3.5 1. ]

[5.5 2.4 3.8 1.1]

[5.5 2.4 3.7 1. ]

[5.8 2.7 3.9 1.2]

[6. 2.7 5.1 1.6]

[5.4 3. 4.5 1.5]

[6. 3.4 4.5 1.6]

[6.7 3.1 4.7 1.5]

[6.3 2.3 4.4 1.3]

[5.6 3. 4.1 1.3]

[5.5 2.5 4. 1.3]

[5.5 2.6 4.4 1.2]

[6.1 3. 4.6 1.4]

[5.8 2.6 4. 1.2]

[5. 2.3 3.3 1. ]

[5.6 2.7 4.2 1.3]

[5.7 3. 4.2 1.2]