从给定的银行贷款分类数据loan_classification_data.csv文件读取数据,对得到的Dataframe,使用boruta方法计算自变量中除loan_id和loan_purpose外其他变量的重要性,并绘制相应的柱状图
时间: 2025-06-09 21:01:50 浏览: 18
首先,我们需要导入必要的库,如pandas、numpy以及用于特征选择的boruta方法。假设你已经安装了`mlxtend`库,可以使用该库提供的`ColumnSelector`和`BorutaPy`来进行数据预处理和特征重要性分析。
```python
import pandas as pd
import numpy as np
from sklearn.datasets import load_boston
from mlxtend.feature_selection import ColumnSelector, BorutaPy
# 加载数据
data = pd.read_csv('loan_classification_data.csv')
# 验证数据是否存在并查看前几行
if 'loan_id' and 'loan_purpose' in data.columns:
print(data.head())
else:
raise ValueError("File doesn't seem to contain the required columns 'loan_id' and 'loan_purpose'.")
# 创建列选择器,排除已知不需要的两列
column_selector = ColumnSelector(columns=['loan_id', 'loan_purpose'])
# 使用BorutaPy计算特征重要性
X = column_selector.fit_transform(data.drop(['loan_id', 'loan_purpose'], axis=1))
boruta = BorutaPy(estimator=None, n_estimators='auto', max_iter=100, perc=75, random_state=42)
boruta.fit(X)
# 获取特征名称和重要性得分
feature_importance = boruta.feature_importances_
selected_features = data.drop(['loan_id', 'loan_purpose'], axis=1).columns[boruta.support_]
# 绘制柱状图
import matplotlib.pyplot as plt
plt.bar(selected_features, feature_importance)
plt.xlabel('Features')
plt.ylabel('Importance Score')
plt.title('Feature Importance using BorutaPy')
plt.show()
阅读全文
相关推荐

















