将 "cross_val_predict" 与 "RepeatedStratifiedKFold" 一起使用会引发错误

Question

我正在尝试绘制 ROC AUC 曲线。我得到的分数如下：

# bagged decision trees on an imbalanced classification problem
from numpy import mean
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
from sklearn.ensemble import BaggingClassifier
# generate dataset
X, y = make_classification(n_samples=10000, n_features=2, n_redundant=0,
    n_clusters_per_class=1, weights=[0.99], flip_y=0, random_state=4)
# define model
model = BaggingClassifier()
# define evaluation procedure
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
# evaluate model
scores = cross_val_score(model, X, y, scoring='roc_auc', cv=cv, n_jobs=-1)
# summarize performance
print('Mean ROC AUC: %.3f' % mean(scores))

但是当我尝试如下调用（用于绘制 ROC AUC 曲线）时：

scores2 = cross_val_predict(model, X, y, cv=cv,method='predict_proba')

我收到错误消息

ValueError: cross_val_predict only works for partitions

请问如何修改绘制曲线的代码？

我在another Whosebug question中发现了一些类似的问题。但目前还没有答案。

Answer 1

使用 cross_val_score(RFmodel, X, y, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise') 因为它不适合，它只有 returns y_predicted 值。

将 "cross_val_predict" 与 "RepeatedStratifiedKFold" 一起使用会引发错误

Using "cross_val_predict" with "RepeatedStratifiedKFold" throws error

python

scikit-learn

cross-validation