如何计算sklearn中每个交叉验证模型的特征重要性

Question

我正在使用 RandomForestClassifier() 和 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())

我想确定我的特征中的重要特征 space。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，我找不到如何在 sklearn 中为 cross validation 执行 feature importance。

总而言之，我想在 10 折交叉验证中确定最有效的特征（例如，通过使用 average importance score）。

如果需要，我很乐意提供更多详细信息。

Answer 1

cross_val_score() 不会 return 训练测试折叠的每个组合的估计量。

您需要使用cross_validate()并设置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

如何计算sklearn中每个交叉验证模型的特征重要性

How to calculate feature importance in each models of cross validation in sklearn

python

machine-learning

scikit-learn

classification

cross-validation