用jupyter写一段代码要求用excel中的数据绘制模型为随机森林的部份依赖图
时间: 2023-06-12 08:03:41 浏览: 76
以下是使用Python的sklearn库和pdpbox库绘制随机森林部分依赖图的代码示例:
首先需要安装sklearn和pdpbox库:
```
!pip install sklearn
!pip install pdpbox
```
接下来,读取Excel文件并进行数据预处理:
```python
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from pdpbox import pdp, get_dataset, info_plots
# 读取Excel文件
df = pd.read_excel('data.xlsx')
# 将分类变量转换成数字
df['Gender'] = df['Gender'].astype('category').cat.codes
df['Married'] = df['Married'].astype('category').cat.codes
df['Education'] = df['Education'].astype('category').cat.codes
df['Self_Employed'] = df['Self_Employed'].astype('category').cat.codes
df['Property_Area'] = df['Property_Area'].astype('category').cat.codes
# 去除不需要的列
X = df.drop(['Loan_Status', 'Loan_ID'], axis=1)
y = df['Loan_Status']
```
接下来,使用随机森林模型进行训练:
```python
# 训练随机森林模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X, y)
```
然后,使用pdpbox库绘制部分依赖图:
```python
# 获取“Credit_History”列的数据集
pdp_credit_history = pdp.pdp_isolate(
model=rf,
dataset=X,
model_features=X.columns.tolist(),
feature='Credit_History'
)
# 绘制部分依赖图
fig, axes = pdp.pdp_plot(
pdp_credit_history,
'Credit_History',
plot_lines=True,
frac_to_plot=100,
plot_pts_dist=True,
x_quantile=True,
show_percentile=False,
plot_params={
'title': 'Credit History and Loan Approval',
'subtitle': 'How does Credit History affect Loan Approval?',
'xaxis_title': 'Credit History',
'yaxis_title': 'Loan Approval'
}
)
```
以上代码将绘制一个标题为“Credit History and Loan Approval”的图表,其中X轴为“Credit History”,Y轴为“Loan Approval”,并显示Credit History对Loan Approval的影响。