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我想用 ROC 曲线评估我的分类模型。我正在努力为交叉验证的数据集计算多类 ROC 曲线。由于交叉验证，训练集和测试集没有划分。在下面，您可以看到我已经尝试过的代码。

   scaler = StandardScaler(with_mean=False) 

   enc = LabelEncoder()
   y = enc.fit_transform(labels)
   vec = DictVectorizer()

   feat_sel = SelectKBest(mutual_info_classif, k=200)    

   n_classes = 3

# Pipeline for computing of ROC curves                 
  clf = OneVsRestClassifier(LogisticRegression(solver='newton-cg', multi_class='multinomial'))
  clf = clf.label_binarizer_
  pipe = Pipeline([('vectorizer', vec),
             ('scaler', scaler),
             ('Logreg', clf),
             ('mutual_info',feat_sel)])

  y_pred = model_selection.cross_val_predict(pipe, instances, y, cv=10) 


  fpr = dict()
  tpr = dict()
  roc_auc = dict()
  for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y[:, i], y_pred[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Plot …