用python写代码采用莺尾花数据画出决策树，形如花瓣长<= 2.45 entropy = 1 .585 samples = 150 value =[50,50, 50] class =山鸢尾 entropy = 0.0 samples = 50 value =[50,0, _0]| class =山鸢尾 花瓣宽<= 1.75entropy = 1.0samples = 100value =[0,50,50]class =变色鸢尾 花瓣长<= 4.95entropy = 0.445samples = 54value =[0,49,5]class =变色鸢尾 花瓣长<= 4.95entropy = 0.151samples = 46value =[0,1, 45]class =维吉尼亚鸢尾 花萼长<= 5.15entropy = 0.146samples = 48value =[0,47,1]class =变色鸢尾 entropy = 0.918samples= 6 value =[0,_ 2, 4]class =维吉尼亚驾尾 entropy = 0.65samples = 6 value =[0,1,5]class =维吉尼亚鸢尾 entropy = 0.0 samples = 40 value =[0,0,40] class =维吉尼亚鸢尾 entropy = 0.722sampies = 5 value =[0,4,1]class =变色鸢尾 entropy = 0.0sampies = 43 value =[0,43,0]class =变色鸢尾

好的，下面是用Python代码实现的决策树绘制过程：

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier, plot_tree

# 加载莺尾花数据集
iris = load_iris()
X = iris.data
y = iris.target

# 定义决策树分类器
clf = DecisionTreeClassifier(random_state=0)

# 训练决策树模型
clf.fit(X, y)

# 绘制决策树
plot_tree(clf, filled=True)

运行上述代码后，就可以绘制出决策树了。如果需要更详细的信息，可以使用`export_text()`函数来打印出文本形式的决策树。