如何计算sklearn中每个交叉验证模型中的特征重要性

Question

我使用RandomForestClassifier()与10 fold cross validation如下。

clf=RandomForestClassifier(random_state = 42, class_weight="balanced")
k_fold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
accuracy = cross_val_score(clf, X, y, cv=k_fold, scoring = 'accuracy')
print(accuracy.mean())

我想确定特征空间中的重要特征。获得单个分类的特征重要性似乎很简单，如下所示。

print("Features sorted by their score:")
feature_importances = pd.DataFrame(clf.feature_importances_,
                                   index = X_train.columns,
                                    columns=['importance']).sort_values('importance', ascending=False)
print(feature_importances)

但是，我怎么也找不到执行feature importance对cross validation在sklearn。

总之，我想average importance score在 10 次交叉验证中确定最有效的特征（例如，通过使用）。

如果需要，我很乐意提供更多详细信息。

Answer 1

cross_val_score() 不返回每个训练测试折叠组合的估计量。

您需要使用cross_validate()和设置return_estimator =True.

这是一个工作示例：

from sklearn import datasets
from sklearn.model_selection import cross_validate
from sklearn.svm import LinearSVC
from sklearn.ensemble import  RandomForestClassifier
import pandas as pd

diabetes = datasets.load_diabetes()
X, y = diabetes.data, diabetes.target

clf=RandomForestClassifier(n_estimators =10, random_state = 42, class_weight="balanced")
output = cross_validate(clf, X, y, cv=2, scoring = 'accuracy', return_estimator =True)

for idx,estimator in enumerate(output['estimator']):
    print("Features sorted by their score for estimator {}:".format(idx))
    feature_importances = pd.DataFrame(estimator.feature_importances_,
                                       index = diabetes.feature_names,
                                        columns=['importance']).sort_values('importance', ascending=False)
    print(feature_importances)

输出：

Features sorted by their score for estimator 0:
     importance
s6     0.137735
age    0.130152
s5     0.114561
s2     0.113683
s3     0.112952
bmi    0.111057
bp     0.108682
s1     0.090763
s4     0.056805
sex    0.023609
Features sorted by their score for estimator 1:
     importance
age    0.129671
bmi    0.125706
s2     0.125304
s1     0.113903
bp     0.111979
s6     0.110505
s5     0.106099
s3     0.098392
s4     0.054542
sex    0.023900