python中'x.sbp','x.tobacco','x.ldl','x.adiposity','x.famhist','x.typea','x.obesity','x.alcohol','x.age'决策树特征重要性可视化
时间: 2023-09-26 13:11:44 浏览: 31
要可视化决策树特征重要性,可以使用sklearn中的`DecisionTreeClassifier`和`plot_importance`函数来实现。具体步骤如下:
1. 导入需要的库和数据
```python
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
data = pd.read_csv('data.csv')
X = data.drop(['y'], axis=1)
y = data['y']
```
2. 训练决策树模型
```python
model = DecisionTreeClassifier()
model.fit(X, y)
```
3. 获取特征重要性值
```python
importance = model.feature_importances_
```
4. 可视化特征重要性值
```python
plt.barh(X.columns, importance)
plt.title("Feature Importances")
plt.xlabel("Importance")
plt.ylabel("Features")
plt.show()
```
完整代码如下:
```python
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
data = pd.read_csv('data.csv')
X = data.drop(['y'], axis=1)
y = data['y']
model = DecisionTreeClassifier()
model.fit(X, y)
importance = model.feature_importances_
plt.barh(X.columns, importance)
plt.title("Feature Importances")
plt.xlabel("Importance")
plt.ylabel("Features")
plt.show()
```
运行代码后,就可以看到特征重要性的可视化图表了。