决策树分类鸢尾花数据集

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier

iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度', u'类别'
path = '8.iris.data'  # 数据文件路径
data = pd.read_csv(path, header=None)
data.columns = iris_feature#将data的每一列的标签设置为iris_feature,如果不设置就默认为0到n的数字
data['类别'] = pd.Categorical(data['类别']).codes#对每一个类别做统计进行打标签赋予数字
x_train = data[['花萼长度', '花瓣长度']]
y_train = data['类别']
model = DecisionTreeClassifier(criterion='entropy', min_samples_leaf=3)
model.fit(x_train, y_train)

N, M = 500, 500  # 横纵各采样多少个值
x1_min, x2_min = x_train.min(axis=0)
x1_max, x2_max = x_train.max(axis=0)
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2)  # 生成网格采样点
x_show = np.stack((x1.flat, x2.flat), axis=1)  # 测试点
y_predict = model.predict(x_show)

mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
cm_light = mpl.colors.ListedColormap(['#A0FFA0', '#FFA0A0', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.pcolormesh(x1, x2, y_predict.reshape(x1.shape), cmap=cm_light)
plt.scatter(x_train['花萼长度'], x_train['花瓣长度'], c=y_train, cmap=cm_dark, marker='o', edgecolors='k')
plt.xlabel('花萼长度')
plt.ylabel('花瓣长度')
plt.title('鸢尾花分类')
plt.grid(True, ls=':')
plt.savefig('1.png')
plt.show()