我有两个变量X和Y。

X的结构（即一个np.array）：

[[26777 24918 26821 ...    -1    -1    -1][26777 26831 26832 ...    -1    -1    -1][26777 24918 26821 ...    -1    -1    -1]...[26811 26832 26813 ...    -1    -1    -1][26830 26831 26832 ...    -1    -1    -1][26830 26831 26832 ...    -1    -1    -1]]

Y的结构：

[[1252, 26777, 26831], [1252, 26777, 26831], [1252, 26777, 26831], [1252, 26777, 26831], [1252, 26777, 26831], [1252, 26777, 26831], [25197, 26777, 26781], [25197, 26777, 26781], [25197, 26777, 26781], [26764, 25803, 26781], [26764, 25803, 26781], [25197, 26777, 26781], [25197, 26777, 26781], [1252, 26777, 16172], [1252, 26777, 16172]]

Y中的数组，例如[1252, 26777, 26831]，是三个独立的特征。

我正在使用scikit learn模块中的Knn分类器

classifier = KNeighborsClassifier(n_neighbors=3)classifier.fit(X,Y)predictions = classifier.predict(X)print(accuracy_score(Y,predictions))

但是我得到了一个错误提示：

ValueError: multiclass-multioutput is not supported

我猜测Y的结构不被支持，为了让程序能够执行，我应该做哪些更改？

输入：

  Deluxe Single room with sea view

期望输出：

c_class = Deluxec_occ = singlec_view = sea

回答：

正如错误中提到的，KNN不支持多输出回归/分类。

对于您的问题，您需要MultiOutputClassifier()。

from sklearn.multioutput import MultiOutputClassifierknn = KNeighborsClassifier(n_neighbors=3)classifier = MultiOutputClassifier(knn, n_jobs=-1)classifier.fit(X,Y)

工作示例：

>>> from sklearn.feature_extraction.text import TfidfVectorizer>>> corpus = [...     'This is the first document.',...     'This document is the second document.',...     'And this is the third one.',...     'Is this the first document?',... ]>>> vectorizer = TfidfVectorizer()>>> X = vectorizer.fit_transform(corpus)>>> Y = [[124323,1234132,1234],[124323,4132,14],[1,4132,1234],[1,4132,14]]>>> from sklearn.multioutput import MultiOutputClassifier>>> from sklearn.neighbors import KNeighborsClassifier>>> knn = KNeighborsClassifier(n_neighbors=3)>>> classifier = MultiOutputClassifier(knn, n_jobs=-1)>>> classifier.fit(X,Y)>>> predictions = classifier.predict(X)array([[124323,   4132,     14],       [124323,   4132,     14],       [     1,   4132,   1234],       [124323,   4132,     14]])>>> classifier.score(X,np.array(Y))0.5>>> test_data = ['I want to test this']>>> classifier.predict(vectorizer.transform(test_data))array([[124323,   4132,     14]])