title: Negative decision_function values
body: <p>I am using support vector classifier from sklearn on the Iris dataset. When I call  <code>decision_function</code> it returns negative values. But all samples in test dataset after classification has right class. I think that decision_function should return the positive value when  the sample is an inlier and negative if the sample is an outlier. Where I am wrong?</p>
  
  <pre><code>from sklearn import datasets
  from sklearn.svm import SVC
  from sklearn.model_selection import train_test_split
  
  iris = datasets.load_iris()
  X = iris.data[:,:]
  y = iris.target
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, 
  random_state=0)
  
  clf = SVC(probability=True)
  print(clf.fit(X_train,y_train).decision_function(X_test))
  print(clf.predict(X_test))
  print(y_test)
  </code></pre>
  
  <p>Here is the output:</p>
  
  <pre><code>[[-0.76231668 -1.03439531 -1.40331645]
   [-1.18273287 -0.64851109  1.50296097]
   [ 1.10803774  1.05572833  0.12956269]
   [-0.47070432 -1.08920859 -1.4647051 ]
   [ 1.18767563  1.12670665  0.21993744]
   [-0.48277866 -0.98796232 -1.83186272]
   [ 1.25020033  1.13721691  0.15514536]
   [-1.07351583 -0.84997114  0.82303659]
   [-1.04709616 -0.85739411  0.64601611]
   [-1.23148923 -0.69072989  1.67459938]
   [-0.77524787 -1.00939817 -1.08441968]
   [-1.12212245 -0.82394879  1.11615504]
   [-1.14646662 -0.91238712  0.80454974]
   [-1.13632316 -0.8812114   0.80171542]
   [-1.14881866 -0.95169643  0.61906248]
   [ 1.15821271  1.10902205  0.22195304]
   [-1.19311709 -0.93149873  0.78649126]
   [-1.21653084 -0.90953622  0.78904491]
   [ 1.16829526  1.12102515  0.20604678]
   [ 1.18446364  1.1080255   0.15199149]
   [-0.93911991 -1.08150089 -0.8026332 ]
   [-1.15462733 -0.95603159  0.5713605 ]
   [ 0.93278883  0.99763184  0.34033663]
   [ 1.10999556  1.04596018  0.14791409]
   [-1.07285663 -1.01864255 -0.10701465]
   [ 1.21200422  1.01284263  0.0416991 ]
   [ 0.9462457   1.01076579  0.36620915]
   [-1.2108146  -0.79124775  1.43264808]
   [-1.02747495 -0.25741977  1.13056021]
  ...
   [ 1.16066886  1.11212424  0.22506538]]
   [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
   2 1 1 2 0 2 0 0]
  
   [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
   1 1 1 2 0 2 0 0]
  </code></pre>