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マルチラベルデータ分類に次のコードを使用しています:-

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.svm import LinearSVC
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn import preprocessing

X_train = np.array(["new york is a hell of a town",
                    "new york was originally dutch",
                    "the big apple is great",
                    "new york is also called the big apple",
                    "nyc is nice",
                    "people abbreviate new york city as nyc",
                    "the capital of great britain is london",
                    "london is in the uk",
                    "london is in england",
                    "london is in great britain",
                    "it rains a lot in london",
                    "london hosts the british museum",
                    "new york is great and so is london",
                    "i like london better than new york"])
y_train_text = [[1],[1],[1],[1],[1],[1],[2],[2],[2],[2],[2],[2],[12],[12]]

X_test = np.array(['nice day in nyc',
                   'welcome to london',
                   'london is rainy',
                   'it is raining in britian',
                   'it is raining in britian and the big apple',
                   'it is raining in britian and nyc',
                   'hello welcome to new york. enjoy it here and london too'])
target_names = ['New York', 'London']

lb = preprocessing.MultiLabelBinarizer()
Y = lb.fit_transform(y_train_text)

classifier = Pipeline([
    ('vectorizer', CountVectorizer()),
    ('tfidf', TfidfTransformer()),
    ('clf', OneVsRestClassifier(LinearSVC()))])

classifier.fit(X_train, Y)
predicted = classifier.predict(X_test)

======出力=====

[1, 0, 0],'New York'
[0, 1, 0],'London'
[0, 1, 0],'London'
[0, 1, 0],'London'
[1, 0, 0],'New York'
[0, 0, 0], 
[0, 0, 0]]

最後の 2 つは間違って予測されています。['New York', 'London'] の場合、どちらも [0,0,1] になるはずです。

だから私はこれらの質問があります:- 1.]私のコードの正確な問題は何ですか? 2.]これは「マルチラベル」データを処理する適切な方法ですか? または、他のより良いアプローチがあります。「マルチラベル」データについてインターネットで見つけることができるのは、これと1つか2つのコードだけだからです。一方、バイナリ分類には数千あります。これについて私を助けてください

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