title: Python : How to find Accuracy Result in SVM Text Classifier Algorithm for Multilabel Class
body: <p>I have used following set of code:
  And I need to check accuracy of X_train and X_test</p>
  
  <p>The following code works for me in my classification problem over multi-labeled class</p>
  
  <pre><code>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
  
  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 = [[0],[0],[0],[0]
              ,[0],[0],[1],[1]
              ,[1],[1],[1],[1]
              ,[2],[2]]
  X_test = np.array(['nice day in nyc',
                     'the capital of great britain is london',
                     'i like london better than new york',
                     ])   
  target_names = ['Class 1', 'Class 2','Class 3']
  
  classifier = Pipeline([
      ('vectorizer', CountVectorizer(min_df=1,max_df=2)),
      ('tfidf', TfidfTransformer()),
      ('clf', OneVsRestClassifier(LinearSVC()))])
  classifier.fit(X_train, y_train)
  predicted = classifier.predict(X_test)
  for item, labels in zip(X_test, predicted):
      print '%s =&gt; %s' % (item, ', '.join(target_names[x] for x in labels))
  </code></pre>
  
  <p><strong>OUTPUT</strong></p>
  
  <pre><code>nice day in nyc =&gt; Class 1
  the capital of great britain is london =&gt; Class 2
  i like london better than new york =&gt; Class 3
  </code></pre>
  
  <p>I would like to check the accuracy between Training and Test Dataset.
  Score Function doesn't work for me, it shows an error stating that multilabel value can't accepted</p>
  
  <pre><code>&gt;&gt;&gt; classifier.score(X_train, X_test)
  </code></pre>
  
  <p><strong>NotImplementedError:</strong> score is not supported for multilabel classifiers</p>
  
  <p>Kindly help me get accuracy results for training and test data and choose an algorithm for our classification case.</p>