在UCI数据集中寻找一个二分类数据集，编程实现决策树分类模型;在划分时可以使用信息增益、增益率或基尼指数进行选择划分属性。要求画出图形。

好的，我明白了你的问题。首先，我需要知道你要使用哪个UCI数据集来进行分类。然后，我可以为你提供一些Python代码来实现决策树分类模型，并且使用信息增益、增益率或基尼指数进行选择划分属性。

假设你选择了UCI的Breast Cancer Wisconsin (Diagnostic) Data Set。那么你可以从下面的链接中下载数据集：

https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(diagnostic)

然后，你可以使用下面的Python代码来实现决策树分类模型，并且使用信息增益、增益率或基尼指数进行选择划分属性：

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import export_graphviz
import graphviz

# Load the dataset
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data'
df = pd.read_csv(url, header=None)

# Split the dataset into features and target
X = df.iloc[:, 2:].values
y = df.iloc[:, 1].values

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create the decision tree classifier
clf = DecisionTreeClassifier(criterion='entropy', random_state=42)

# Fit the decision tree classifier to the training data
clf.fit(X_train, y_train)

# Use the decision tree classifier to make predictions on the testing data
y_pred = clf.predict(X_test)

# Calculate the accuracy of the decision tree classifier
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

# Export the decision tree as a Graphviz file
dot_data = export_graphviz(clf, out_file=None, 
                     feature_names=df.columns[2:],  
                     class_names=df.columns[1],
                     filled=True, rounded=True,  
                     special_characters=True)
graph = graphviz.Source(dot_data)
graph.render('breast_cancer_diagnostic_tree')

这段代码会将数据集加载到Pandas DataFrame中，并将其拆分为特征和目标。然后，它将数据集拆分为训练集和测试集，并使用信息增益作为划分准则来创建决策树分类器。最后，它将决策树以Graphviz格式导出为一个文件，并将其保存为“breast_cancer_diagnostic_tree.pdf”文件。

你可以使用Graphviz软件来查看决策树。如果你没有安装Graphviz，你可以使用下面的代码来在Jupyter Notebook中显示决策树：

from IPython.display import Image
Image(filename='breast_cancer_diagnostic_tree.png')

这段代码会在Jupyter Notebook中显示决策树的图形。