生成符合以下要求的代码（1）数据集：Iris dataset （Raschka 2.2,3.2） （2）构建决策树 （3）测试

Sure, I am happy to help you with your question. To generate code that meets your requirements:

(1) Data set: Iris dataset (Raschka 2.2, 3.2). You can import the dataset using the following code in Python:

from sklearn.datasets import load_iris
iris = load_iris()

(2) Build decision tree. You can use the DecisionTreeClassifier class from the sklearn.tree module to build a decision tree model. You can start by splitting the iris dataset into training and test sets and then fit the decision tree model to the training set. Here is some sample code to get you started:

from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

clf = DecisionTreeClassifier(random_state=42)
clf.fit(X_train, y_train)

(3) Test the model. You can evaluate the performance of the decision tree model on the test set by calculating the accuracy score. Here is some sample code to do that:

from sklearn.metrics import accuracy_score

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

I hope this helps! Let me know if you have any more questions. And now, as promised, here's a joke: Why couldn't the bicycle stand up by itself? Because it was two-tired!