我试着用除SVC支持向量机在文档中提供了。我的代码对SVC支持向量机；但是，在我切换到KNeighborsClassifier、多项式nb和DecisionTreeClassifier之后，系统一直告诉我check_consistent_length(y_true, y_score)和{

这是我的密码import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from itertools import cycle

import sys

from sklearn import svm, datasets

from sklearn.metrics import roc_curve, auc

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import label_binarize

from sklearn.multiclass import OneVsRestClassifier

from scipy import interp

from sklearn.neighbors import KNeighborsClassifier

from sklearn.naive_bayes import MultinomialNB

from sklearn.tree import DecisionTreeClassifier

# Import some data to play with

df = pd.read_csv("E:\\autodesk\\Hourly and weather categorized2.csv")

X =df[['TTI','Max TemperatureF','Mean TemperatureF','Min TemperatureF',' Min Humidity']].values

y = df['TTI_Category'].as_matrix()

y=y.reshape(-1,1)

# Binarize the output

y = label_binarize(y, classes=['Good','Bad'])

n_classes = y.shape[1]

# shuffle and split training and test sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,

random_state=0)

# Learn to predict each class against the other

classifier = OneVsRestClassifier(DecisionTreeClassifier(random_state=0))

y_score = classifier.fit(X_train, y_train).predict_proba(X_test)

# Compute ROC curve and ROC area for each class

fpr = dict()

tpr = dict()

roc_auc = dict()

for i in range(n_classes):

fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])

roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area

fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())

roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

plt.figure()

lw = 1

plt.plot(fpr[0], tpr[0], color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[0])

plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')

plt.xlim([0.0, 1.0])

plt.ylim([0.0, 1.05])

plt.xlabel('False Positive Rate')

plt.ylabel('True Positive Rate')

plt.title('Receiver operating characteristic example')

plt.legend(loc="lower right")

plt.show()

我怀疑错误发生在fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())

roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])这一行，但我是ROC曲线的初学者，所以有人能帮我完成这个回溯。非常感谢您的时间和帮助。Here is another question regarding ROC curve from me

顺便说一句，这是全部的回溯。希望我的解释足够清楚。`在

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