机器学习决策树鸢尾花数据集，绘制决策边界，出现相同代码相同数据多次运行，结果不一致的问题

Question

练习机器学习中，采用决策树将鸢尾花的数据进行分类，并绘制决策边界，代码如下：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris()
x = iris.data[:,2:]
y = iris.target
from sklearn.tree import DecisionTreeClassifier
dt_clf = DecisionTreeClassifier(max_depth = 2,criterion = 'entropy')
dt_clf.fit(x,y)

def plot_decision_boundary(model,axis):
    x0,x1 = np.meshgrid(
        np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*200)),
        np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*200))
    )
    
    x_new = np.c_[x0.ravel(),x1.ravel()]
    y_predict = model.predict(x_new)
    zz = y_predict.reshape(x0.shape)
    
    from matplotlib.colors import ListedColormap
    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    plt.contourf(x0,x1,zz,cmap = custom_cmap)

plot_decision_boundary(dt_clf,axis= [0.5,7.5,0,3])
plt.scatter(x[y==0,0],x[y==0,1])
plt.scatter(x[y==1,0],x[y==1,1])
plt.scatter(x[y==2,0],x[y==2,1])
plt.show() # 这个结果有点不对 ，但我又不知道哪里搞错了

第一次运行出现了下图所示的分类结果：

第二次及以后运行时出现了下图的分类结果：

我想知道明明是相同的数据，相同的代码，只是运行先后顺序不同，为什么会出现上下两个图之间的完全不同的分类结果，并且出现哪种分类结果还有一定的随机性？我的代码里也没有随机数。虽然非参数学习对于数据依赖非常严重，但是我的数据也没有发生更改啊，很奇怪。

Answer 1

sklearn.tree.DecisionTreeClassifier()在进行分支的时候特征选择是随机的，即使是splitter=”best”的时候。打印dt_clf.feature_importances_的话就会看到有两种不同的结果，对应两种决策边界。sklearn.tree.DecisionTreeClassifier的函数说明中明确说：

random_state：int, RandomState instance or None, default=None
Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See Glossary for details.