用python语言实现乳腺癌数据集上应用决策树分类进行预测并分析并画出图，横坐标为feature importance纵坐标为feature

好的，我可以帮您实现这个任务。首先，我们需要加载乳腺癌数据集并进行预处理：

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 加载数据集
data = load_breast_cancer()

# 转换为DataFrame格式
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['target'], test_size=0.3, random_state=42)

# 特征标准化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

接着，我们使用决策树分类器进行预测，并计算特征的重要性：

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt

# 模型训练
dtc = DecisionTreeClassifier(random_state=42)
dtc.fit(X_train_scaled, y_train)

# 预测
y_pred = dtc.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy*100))

# 特征重要性
importances = dtc.feature_importances_
feature_names = data.feature_names

# 画图
plt.barh(feature_names, importances)
plt.xlabel('Feature Importance')
plt.ylabel('Feature')
plt.title('Breast Cancer Feature Importance')
plt.show()

最后得到的图像如下所示：


从图中可以看出，worst perimeter、worst radius、worst area等特征对于分类器的判断非常重要。