像这样的东西应该让你去。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


df = pd.read_csv("https://rodeo-tutorials.s3.amazonaws.com/data/credit-data-trainingset.csv")
df.head()

from sklearn.ensemble import RandomForestClassifier

features = np.array(['revolving_utilization_of_unsecured_lines',
                     'age', 'number_of_time30-59_days_past_due_not_worse',
                     'debt_ratio', 'monthly_income','number_of_open_credit_lines_and_loans', 
                     'number_of_times90_days_late', 'number_real_estate_loans_or_lines',
                     'number_of_time60-89_days_past_due_not_worse', 'number_of_dependents'])
clf = RandomForestClassifier()
clf.fit(df[features], df['serious_dlqin2yrs'])

# from the calculated importances, order them from most to least important
# and make a barplot so we can visualize what is/isn't important
importances = clf.feature_importances_
sorted_idx = np.argsort(importances)


padding = np.arange(len(features)) + 0.5
plt.barh(padding, importances[sorted_idx], align='center')
plt.yticks(padding, features[sorted_idx])
plt.xlabel("Relative Importance")
plt.title("Variable Importance")
plt.show()

假设我们在这里讨论的是决策树算法的特征重要性——你真的不能说。它仅告诉您使用某个功能将两个类分开的频率。

如果您想更深入地了解您的模型如何做出决策，您可以查看SHAP和LIME。两者都是近似您的模型然后尝试解释它的方法。您可以在 Python 中查看这两个库。