机器学习 | Kaggle鸢尾花数据集Iris训练_kaggle鸢尾花竞赛题目

机器学习 | Kaggle鸢尾花数据集Iris训练

​ Wenxuan Zeng 2020.10.3

一、准备工作：引入机器学习库

# 引入机器学习库
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelBinarizer
from sklearn import svm
from sklearn import model_selection
from sklearn import metrics 
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# 忽略不必要的报错
import warnings
warnings.filterwarnings("ignore")
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二、数据可视化分析

• 该题数据量很小（150组）特征值不多（4类）标签简单（3种），且题目要求在于分类，所以在做数据可视化分析的时候，不需要像考虑Titanic/Crime_prediction那样深度挖掘不同特征之间微妙的联系，我们直接分情况将散点图绘制在同一坐标系下即可观察。

# 载入sklearn库中的iris数据集
from sklearn.datasets import load_iris
iris = load_iris()
print (iris.data)
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[[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]
 [5.7 2.9 4.2 1.3]
 [6.2 2.9 4.3 1.3]
 [5.1 2.5 3.  1.1]
 [5.7 2.8 4.1 1.3]
 [6.3 3.3 6.  2.5]
 [5.8 2.7 5.1 1.9]
 [7.1 3.  5.9 2.1]
 [6.3 2.9 5.6 1.8]
 [6.5 3.  5.8 2.2]
 [7.6 3.  6.6 2.1]
 [4.9 2.5 4.5 1.7]
 [7.3 2.9 6.3 1.8]
 [6.7 2.5 5.8 1.8]
 [7.2 3.6 6.1 2.5]
 [6.5 3.2 5.1 2. ]
 [6.4 2.7 5.3 1.9]
 [6.8 3.  5.5 2.1]
 [5.7 2.5 5.  2. ]
 [5.8 2.8 5.1 2.4]
 [6.4 3.2 5.3 2.3]
 [6.5 3.  5.5 1.8]
 [7.7 3.8 6.7 2.2]
 [7.7 2.6 6.9 2.3]
 [6.  2.2 5.  1.5]
 [6.9 3.2 5.7 2.3]
 [5.6 2.8 4.9 2. ]
 [7.7 2.8 6.7 2. ]
 [6.3 2.7 4.9 1.8]
 [6.7 3.3 5.7 2.1]
 [7.2 3.2 6.  1.8]
 [6.2 2.8 4.8 1.8]
 [6.1 3.  4.9 1.8]
 [6.4 2.8 5.6 2.1]
 [7.2 3.  5.8 1.6]
 [7.4 2.8 6.1 1.9]
 [7.9 3.8 6.4 2. ]
 [6.4 2.8 5.6 2.2]
 [6.3 2.8 5.1 1.5]
 [6.1 2.6 5.6 1.4]
 [7.7 3.  6.1 2.3]
 [6.3 3.4 5.6 2.4]
 [6.4 3.1 5.5 1.8]
 [6.  3.  4.8 1.8]
 [6.9 3.1 5.4 2.1]
 [6.7 3.1 5.6 2.4]
 [6.9 3.1 5.1 2.3]
 [5.8 2.7 5.1 1.9]
 [6.8 3.2 5.9 2.3]
 [6.7 3.3 5.7 2.5]
 [6.7 3.  5.2 2.3]
 [6.3 2.5 5.  1.9]
 [6.5 3.  5.2 2. ]
 [6.2 3.4 5.4 2.3]
 [5.9 3.  5.1 1.8]]
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# iris的三个种类 各有50个 共150个
print (iris.target)
print (len(iris.target))
# 共150个数据 每个iris有4个特征属性
print (iris.data.shape)
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[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2]
150
(150, 4)
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• iris中包含iris.data和iris.target，其中iris.data是150x4的矩阵，存放四特征（花瓣和花萼长宽值），iris.target是150x1的矩阵，存放150朵花的标签（类别：0，1，2）。这三种类别的花分别50朵，分布在数据集的前50，中50，后50.

# 获取花卉一二列特征数据集（花萼特征）
DD = iris.data  
X = [x[0] for x in DD]  
print (X)  
Y = [x[1] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()
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[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]
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• 该组出现的问题即是：选取花萼宽度和长度特征并不能有效将蓝色和绿色离散点分开，所以相关度较低。

# 获取花卉三四列特征数据集  （花瓣特征）
DD = iris.data  
X = [x[2] for x in DD]  
print (X)  
Y = [x[3] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()
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[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]
[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

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# 获取花卉一四列特征数据集  
DD = iris.data  
X = [x[0] for x in DD]  
print (X)  
Y = [x[3] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()

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[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

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# 获取花卉一三列特征数据集  
DD = iris.data  
X = [x[0] for x in DD]  
print (X)  
Y = [x[2] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()

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[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]
[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]

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# 获取花卉二三列特征数据集  
DD = iris.data  
X = [x[1] for x in DD]  
print (X)  
Y = [x[2] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()

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[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]
[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]

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# 获取花卉二四列特征数据集  
DD = iris.data  
X = [x[1] for x in DD]  
print (X)  
Y = [x[3] for x in DD]  
print (Y)  
  
#plt.scatter(X, Y, c=iris.target, marker='x')
# 第一类 前50个样本
plt.scatter(X[:50], Y[:50], color='red', marker='o', label='setosa')
# 第二类 中间50个样本
plt.scatter(X[50:100], Y[50:100], color='blue', marker='x', label='versicolor') 
# 第三类 后50个样本
plt.scatter(X[100:], Y[100:],color='green', marker='+', label='Virginica')
# 图例
plt.legend(loc=2) #左上角
plt.show()

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[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]
[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]

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三、逻辑回归分类结果

3.1 选取花萼特征分类

# 获取花卉前两列数据集
X = X = iris.data[:, 0:2]   
Y = iris.target           

# 逻辑回归模型
lr = LogisticRegression(C=1e5)  
lr.fit(X,Y)

# meshgrid函数生成两个网格矩阵
h = .02
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

# pcolormesh函数将xx,yy两个网格矩阵和对应的预测结果Z绘制在图片上
Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(8,6))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

# 绘制散点图
plt.scatter(X[:50,0], X[:50,1], color='red',marker='o', label='setosa')
plt.scatter(X[50:100,0], X[50:100,1], color='blue', marker='x', label='versicolor')
plt.scatter(X[100:,0], X[100:,1], color='green', marker='s', label='Virginica') 

plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.legend(loc=2) 
plt.show()

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3.2 选取花瓣特征分类

# 获取花卉后两列数据集
X = X = iris.data[:, 2:4]   
Y = iris.target           

# 逻辑回归模型
lr = LogisticRegression(C=1e5)  
lr.fit(X,Y)

# meshgrid函数生成两个网格矩阵
h = .02
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

# pcolormesh函数将xx,yy两个网格矩阵和对应的预测结果Z绘制在图片上
Z = lr.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(8,6))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

# 绘制散点图
plt.scatter(X[:50,0], X[:50,1], color='red',marker='o', label='setosa')
plt.scatter(X[50:100,0], X[50:100,1], color='blue', marker='x', label='versicolor')
plt.scatter(X[100:,0], X[100:,1], color='green', marker='s', label='Virginica') 

plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
plt.legend(loc=2) 
plt.show()

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四、数据集切割（训练集+测试集）

x=iris.data
y=iris.target
x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3)
print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape)

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split_train_data 70%: (105, 4) split_train_target 70%: (105,) split_test_data 30% (45, 4) split_test_target 30% (45,)

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五、模型训练并用验证集验证

5.1 逻辑回归

# Logistic Regression
model = LogisticRegression()
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the Logistic Regression is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the Logistic Regression is: 0.9555555555555556

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5.2 决策树

# DecisionTreeClassifier
model=DecisionTreeClassifier()
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the DecisionTreeClassifier is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the DecisionTreeClassifier is: 0.9555555555555556

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5.3 K-邻近

# K-Nearest Neighbours
model=KNeighborsClassifier(n_neighbors=3)
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the K-Nearest Neighbours is: 1.0

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5.4 支持向量机

# Support Vector Machine
model = svm.SVC()
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the SVM is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the SVM is: 1.0

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六、神经网络

x=iris.data
y=iris.target
np.random.seed(seed=7)
y_Label=LabelBinarizer().fit_transform(y)
x_train,y_train,x_test,y_test=model_selection.train_test_split(x,y_Label,test_size=0.3,random_state=42)

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from keras.models import Sequential
from keras.layers.core import Dense
model = Sequential() #建立模型

model.add(Dense(4,activation='relu',input_shape=(4,)))
model.add(Dense(6,activation='relu'))
model.add(Dense(3,activation='softmax'))

model.summary()

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Model: "sequential_23"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_68 (Dense)             (None, 4)                 20        
_________________________________________________________________
dense_69 (Dense)             (None, 6)                 30        
_________________________________________________________________
dense_70 (Dense)             (None, 3)                 21        
=================================================================
Total params: 71
Trainable params: 71
Non-trainable params: 0
_________________________________________________________________

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model.compile(loss='categorical_crossentropy',optimizer='rmsprop',metrics=['accuracy'])

step=25
history=model.fit(x_train,x_test,validation_data=(y_train,y_test),batch_size=10,epochs=step)
train_result=history.history

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Train on 105 samples, validate on 45 samples
Epoch 1/25
105/105 [==============================] - 0s 2ms/step - loss: 0.1160 - accuracy: 0.9429 - val_loss: 0.0453 - val_accuracy: 1.0000
Epoch 2/25
105/105 [==============================] - 0s 123us/step - loss: 0.1121 - accuracy: 0.9524 - val_loss: 0.0486 - val_accuracy: 1.0000
Epoch 3/25
105/105 [==============================] - 0s 114us/step - loss: 0.1116 - accuracy: 0.9429 - val_loss: 0.0496 - val_accuracy: 1.0000
Epoch 4/25
105/105 [==============================] - 0s 114us/step - loss: 0.1129 - accuracy: 0.9524 - val_loss: 0.0479 - val_accuracy: 1.0000
Epoch 5/25
105/105 [==============================] - 0s 123us/step - loss: 0.1136 - accuracy: 0.9524 - val_loss: 0.0483 - val_accuracy: 1.0000
Epoch 6/25
105/105 [==============================] - 0s 114us/step - loss: 0.1112 - accuracy: 0.9524 - val_loss: 0.0518 - val_accuracy: 1.0000
Epoch 7/25
105/105 [==============================] - 0s 114us/step - loss: 0.1110 - accuracy: 0.9429 - val_loss: 0.0505 - val_accuracy: 1.0000
Epoch 8/25
105/105 [==============================] - 0s 123us/step - loss: 0.1099 - accuracy: 0.9524 - val_loss: 0.0467 - val_accuracy: 1.0000
Epoch 9/25
105/105 [==============================] - 0s 114us/step - loss: 0.1109 - accuracy: 0.9524 - val_loss: 0.0554 - val_accuracy: 0.9778
Epoch 10/25
105/105 [==============================] - 0s 123us/step - loss: 0.1097 - accuracy: 0.9524 - val_loss: 0.0535 - val_accuracy: 1.0000
Epoch 11/25
105/105 [==============================] - 0s 114us/step - loss: 0.1091 - accuracy: 0.9524 - val_loss: 0.0452 - val_accuracy: 1.0000
Epoch 12/25
105/105 [==============================] - 0s 123us/step - loss: 0.1040 - accuracy: 0.9619 - val_loss: 0.0742 - val_accuracy: 0.9778
Epoch 13/25
105/105 [==============================] - 0s 123us/step - loss: 0.1124 - accuracy: 0.9524 - val_loss: 0.0670 - val_accuracy: 0.9778
Epoch 14/25
105/105 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.90 - 0s 133us/step - loss: 0.1110 - accuracy: 0.9429 - val_loss: 0.0746 - val_accuracy: 0.9778
Epoch 15/25
105/105 [==============================] - 0s 114us/step - loss: 0.1105 - accuracy: 0.9429 - val_loss: 0.0545 - val_accuracy: 0.9778
Epoch 16/25
105/105 [==============================] - 0s 123us/step - loss: 0.1077 - accuracy: 0.9524 - val_loss: 0.0519 - val_accuracy: 1.0000
Epoch 17/25
105/105 [==============================] - 0s 114us/step - loss: 0.1113 - accuracy: 0.9524 - val_loss: 0.0562 - val_accuracy: 0.9778
Epoch 18/25
105/105 [==============================] - 0s 123us/step - loss: 0.1085 - accuracy: 0.9524 - val_loss: 0.0491 - val_accuracy: 1.0000
Epoch 19/25
105/105 [==============================] - 0s 123us/step - loss: 0.1101 - accuracy: 0.9524 - val_loss: 0.0548 - val_accuracy: 0.9778
Epoch 20/25
105/105 [==============================] - 0s 114us/step - loss: 0.1060 - accuracy: 0.9429 - val_loss: 0.0451 - val_accuracy: 1.0000
Epoch 21/25
105/105 [==============================] - 0s 123us/step - loss: 0.1128 - accuracy: 0.9524 - val_loss: 0.0450 - val_accuracy: 1.0000
Epoch 22/25
105/105 [==============================] - 0s 123us/step - loss: 0.1086 - accuracy: 0.9524 - val_loss: 0.0534 - val_accuracy: 0.9778
Epoch 23/25
105/105 [==============================] - 0s 123us/step - loss: 0.1065 - accuracy: 0.9524 - val_loss: 0.0455 - val_accuracy: 1.0000
Epoch 24/25
105/105 [==============================] - 0s 114us/step - loss: 0.1062 - accuracy: 0.9524 - val_loss: 0.0434 - val_accuracy: 1.0000
Epoch 25/25
105/105 [==============================] - 0s 123us/step - loss: 0.1081 - accuracy: 0.9524 - val_loss: 0.0423 - val_accuracy: 1.0000

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• 基于keras的全连接神经网络模型，仅仅需要很少的训练次数，即可达到100%的准确度，是训练iris数据集时不错的选择。

# 从最终的训练模型种读取准确度
acc=train_result['accuracy']
val_acc=train_result['val_accuracy']
epochs=range(1,step+1)
plt.plot(epochs,acc,'b-')
plt.plot(epochs,val_acc,'r')
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.show()

t=model.predict(y_train)
resultsss=model.evaluate(y_train,y_test)
resultsss

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45/45 [==============================] - 0s 44us/step
[0.5691331187884013, 0.9111111164093018]

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七、将花瓣和花萼特征分离+标签 训练

7.1 选取花萼特征训练

# 获取花卉一二列特征数据集  
DD = iris.data  
x=DD[ :,0:2]
print(x)
y=iris.target

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[[5.1 3.5]
 [4.9 3. ]
 [4.7 3.2]
 [4.6 3.1]
 [5.  3.6]
 [5.4 3.9]
 [4.6 3.4]
 [5.  3.4]
 [4.4 2.9]
 [4.9 3.1]
 [5.4 3.7]
 [4.8 3.4]
 [4.8 3. ]
 [4.3 3. ]
 [5.8 4. ]
 [5.7 4.4]
 [5.4 3.9]
 [5.1 3.5]
 [5.7 3.8]
 [5.1 3.8]
 [5.4 3.4]
 [5.1 3.7]
 [4.6 3.6]
 [5.1 3.3]
 [4.8 3.4]
 [5.  3. ]
 [5.  3.4]
 [5.2 3.5]
 [5.2 3.4]
 [4.7 3.2]
 [4.8 3.1]
 [5.4 3.4]
 [5.2 4.1]
 [5.5 4.2]
 [4.9 3.1]
 [5.  3.2]
 [5.5 3.5]
 [4.9 3.6]
 [4.4 3. ]
 [5.1 3.4]
 [5.  3.5]
 [4.5 2.3]
 [4.4 3.2]
 [5.  3.5]
 [5.1 3.8]
 [4.8 3. ]
 [5.1 3.8]
 [4.6 3.2]
 [5.3 3.7]
 [5.  3.3]
 [7.  3.2]
 [6.4 3.2]
 [6.9 3.1]
 [5.5 2.3]
 [6.5 2.8]
 [5.7 2.8]
 [6.3 3.3]
 [4.9 2.4]
 [6.6 2.9]
 [5.2 2.7]
 [5.  2. ]
 [5.9 3. ]
 [6.  2.2]
 [6.1 2.9]
 [5.6 2.9]
 [6.7 3.1]
 [5.6 3. ]
 [5.8 2.7]
 [6.2 2.2]
 [5.6 2.5]
 [5.9 3.2]
 [6.1 2.8]
 [6.3 2.5]
 [6.1 2.8]
 [6.4 2.9]
 [6.6 3. ]
 [6.8 2.8]
 [6.7 3. ]
 [6.  2.9]
 [5.7 2.6]
 [5.5 2.4]
 [5.5 2.4]
 [5.8 2.7]
 [6.  2.7]
 [5.4 3. ]
 [6.  3.4]
 [6.7 3.1]
 [6.3 2.3]
 [5.6 3. ]
 [5.5 2.5]
 [5.5 2.6]
 [6.1 3. ]
 [5.8 2.6]
 [5.  2.3]
 [5.6 2.7]
 [5.7 3. ]
 [5.7 2.9]
 [6.2 2.9]
 [5.1 2.5]
 [5.7 2.8]
 [6.3 3.3]
 [5.8 2.7]
 [7.1 3. ]
 [6.3 2.9]
 [6.5 3. ]
 [7.6 3. ]
 [4.9 2.5]
 [7.3 2.9]
 [6.7 2.5]
 [7.2 3.6]
 [6.5 3.2]
 [6.4 2.7]
 [6.8 3. ]
 [5.7 2.5]
 [5.8 2.8]
 [6.4 3.2]
 [6.5 3. ]
 [7.7 3.8]
 [7.7 2.6]
 [6.  2.2]
 [6.9 3.2]
 [5.6 2.8]
 [7.7 2.8]
 [6.3 2.7]
 [6.7 3.3]
 [7.2 3.2]
 [6.2 2.8]
 [6.1 3. ]
 [6.4 2.8]
 [7.2 3. ]
 [7.4 2.8]
 [7.9 3.8]
 [6.4 2.8]
 [6.3 2.8]
 [6.1 2.6]
 [7.7 3. ]
 [6.3 3.4]
 [6.4 3.1]
 [6.  3. ]
 [6.9 3.1]
 [6.7 3.1]
 [6.9 3.1]
 [5.8 2.7]
 [6.8 3.2]
 [6.7 3.3]
 [6.7 3. ]
 [6.3 2.5]
 [6.5 3. ]
 [6.2 3.4]
 [5.9 3. ]]

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x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3)
print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape)

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split_train_data 70%: (105, 2) split_train_target 70%: (105,) split_test_data 30% (45, 2) split_test_target 30% (45,)

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# K-Nearest Neighbours
model=KNeighborsClassifier(n_neighbors=3)
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the K-Nearest Neighbours is: 0.6444444444444445

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7.1 选取花瓣特征训练

DD = iris.data  
x=DD[ :,2:4]
print(x)

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[[1.4 0.2]
 [1.4 0.2]
 [1.3 0.2]
 [1.5 0.2]
 [1.4 0.2]
 [1.7 0.4]
 [1.4 0.3]
 [1.5 0.2]
 [1.4 0.2]
 [1.5 0.1]
 [1.5 0.2]
 [1.6 0.2]
 [1.4 0.1]
 [1.1 0.1]
 [1.2 0.2]
 [1.5 0.4]
 [1.3 0.4]
 [1.4 0.3]
 [1.7 0.3]
 [1.5 0.3]
 [1.7 0.2]
 [1.5 0.4]
 [1.  0.2]
 [1.7 0.5]
 [1.9 0.2]
 [1.6 0.2]
 [1.6 0.4]
 [1.5 0.2]
 [1.4 0.2]
 [1.6 0.2]
 [1.6 0.2]
 [1.5 0.4]
 [1.5 0.1]
 [1.4 0.2]
 [1.5 0.2]
 [1.2 0.2]
 [1.3 0.2]
 [1.4 0.1]
 [1.3 0.2]
 [1.5 0.2]
 [1.3 0.3]
 [1.3 0.3]
 [1.3 0.2]
 [1.6 0.6]
 [1.9 0.4]
 [1.4 0.3]
 [1.6 0.2]
 [1.4 0.2]
 [1.5 0.2]
 [1.4 0.2]
 [4.7 1.4]
 [4.5 1.5]
 [4.9 1.5]
 [4.  1.3]
 [4.6 1.5]
 [4.5 1.3]
 [4.7 1.6]
 [3.3 1. ]
 [4.6 1.3]
 [3.9 1.4]
 [3.5 1. ]
 [4.2 1.5]
 [4.  1. ]
 [4.7 1.4]
 [3.6 1.3]
 [4.4 1.4]
 [4.5 1.5]
 [4.1 1. ]
 [4.5 1.5]
 [3.9 1.1]
 [4.8 1.8]
 [4.  1.3]
 [4.9 1.5]
 [4.7 1.2]
 [4.3 1.3]
 [4.4 1.4]
 [4.8 1.4]
 [5.  1.7]
 [4.5 1.5]
 [3.5 1. ]
 [3.8 1.1]
 [3.7 1. ]
 [3.9 1.2]
 [5.1 1.6]
 [4.5 1.5]
 [4.5 1.6]
 [4.7 1.5]
 [4.4 1.3]
 [4.1 1.3]
 [4.  1.3]
 [4.4 1.2]
 [4.6 1.4]
 [4.  1.2]
 [3.3 1. ]
 [4.2 1.3]
 [4.2 1.2]
 [4.2 1.3]
 [4.3 1.3]
 [3.  1.1]
 [4.1 1.3]
 [6.  2.5]
 [5.1 1.9]
 [5.9 2.1]
 [5.6 1.8]
 [5.8 2.2]
 [6.6 2.1]
 [4.5 1.7]
 [6.3 1.8]
 [5.8 1.8]
 [6.1 2.5]
 [5.1 2. ]
 [5.3 1.9]
 [5.5 2.1]
 [5.  2. ]
 [5.1 2.4]
 [5.3 2.3]
 [5.5 1.8]
 [6.7 2.2]
 [6.9 2.3]
 [5.  1.5]
 [5.7 2.3]
 [4.9 2. ]
 [6.7 2. ]
 [4.9 1.8]
 [5.7 2.1]
 [6.  1.8]
 [4.8 1.8]
 [4.9 1.8]
 [5.6 2.1]
 [5.8 1.6]
 [6.1 1.9]
 [6.4 2. ]
 [5.6 2.2]
 [5.1 1.5]
 [5.6 1.4]
 [6.1 2.3]
 [5.6 2.4]
 [5.5 1.8]
 [4.8 1.8]
 [5.4 2.1]
 [5.6 2.4]
 [5.1 2.3]
 [5.1 1.9]
 [5.9 2.3]
 [5.7 2.5]
 [5.2 2.3]
 [5.  1.9]
 [5.2 2. ]
 [5.4 2.3]
 [5.1 1.8]]

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x_train,x_test,y_train,y_test=model_selection.train_test_split(x,y,random_state=101,test_size=0.3)
print("split_train_data 70%:", x_train.shape, "split_train_target 70%:",y_train.shape, "split_test_data 30%", x_test.shape, "split_test_target 30%",y_test.shape)

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split_train_data 70%: (105, 2) split_train_target 70%: (105,) split_test_data 30% (45, 2) split_test_target 30% (45,)

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# K-Nearest Neighbours
model=KNeighborsClassifier(n_neighbors=3)
model.fit(x_train, y_train)
prediction=model.predict(x_test)
print('The accuracy of the K-Nearest Neighbours is: {0}'.format(metrics.accuracy_score(prediction,y_test)))

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The accuracy of the K-Nearest Neighbours is: 0.9777777777777777

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• 由此可见，同样选取了准确率很高的KNN模型，选取花萼特征和花瓣特征分别进行训练，得到的预测准确度是不同的。花萼特征相关度较低，花瓣特征相关度较高，花瓣训练结果较为乐观，而四个特征一起加入训练时准确率最高，可以达到100%.