利用CART决策树对iris数据集进行分类。训练数据集占原数据集的90%，剩余的为测试数据集。画出决策树模型后评估模型准确率。

好的，我会为您解答这个问题。

首先，我们需要加载iris数据集并将其分为训练集和测试集：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1)

接下来，我们可以使用CART决策树来训练模型：

from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

然后，我们可以使用测试集来评估模型的准确率：

from sklearn.metrics import accuracy_score
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

最后，我们可以使用Graphviz来可视化决策树：

from sklearn.tree import export_graphviz
import graphviz
dot_data = export_graphviz(model, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names)
graph = graphviz.Source(dot_data)
graph.render("iris")

这将生成一个名为“iris”的PDF文件，其中包含决策树的可视化。

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.tree import export_graphviz
import graphviz

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.1)

model = DecisionTreeClassifier()
model.fit(X_train, y_train)

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

dot_data = export_graphviz(model, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names)
graph = graphviz.Source(dot_data)
graph.render("iris")