title: How do I get more decimal in clf.predict_proba(X_test)?
body: <p>I have a pandas dataframe, for a binary classification case (category A, and category B). To get  <code>X_train, X_test, y_train, y_test</code> I do 70:30 split like this:</p>
  
  <pre><code>from sklearn.model_selection import train_test_split
  target = pd.DataFrame(data['good'])
  features = data.drop('good', axis=1)
  X_train, X_test, y_train, y_test = train_test_split(features, 
                                                      target, 
                                                      test_size = 0.3, 
                                                      random_state = 0)
  </code></pre>
  
  <p>Then I did RandomForest classifier, with that this code</p>
  
  <pre><code>from sklearn.ensemble import RandomForestClassifier
  clf = RandomForestClassifier(n_jobs=2, random_state=0)
  model = clf.fit(X_train, y_train)
  </code></pre>
  
  <p>As usual you can do get the prediction by doing <code>clf.predict(X_test)</code>. It give <code>numpy.ndarray</code> object like this</p>
  
  <pre><code>array(['0', '0', '1', '0', '0', '1', '0', '1', '1', '1'], dtype=object)
  </code></pre>
  
  <p>Then, I want to calculate prediction probabiblity by <code>clf.predict_proba(X_test)</code> and the result is</p>
  
  <pre><code>array([[ 0.7  ,  0.3  ],
         [ 0.8  ,  0.2  ],
         [ 0.4  ,  0.6  ],
         [ 0.8  ,  0.2  ],
         [ 0.5  ,  0.5  ],
         [ 0.1  ,  0.9  ],
         [ 0.5  ,  0.5  ],
         [ 0.3  ,  0.7  ],
         [ 0.3  ,  0.7  ],
         [ 0.5  ,  0.5  ]])
  </code></pre>
  
  <p>I want to get more decimal in <code>clf.predict_proba(X_test)</code> output. (I am expected 3 decimals) For example,</p>
  
  <pre><code>array([[ 0.712  ,  0.288  ],
         [ 0.845  ,  0.155  ... etc
  </code></pre>
  
  <p>It would be better if the answer also convert and merge <code>clf.predict(X_test)</code> and <code>clf.predict_proba(X_test)</code> to pandas dataframe as well, because I would continue to calculate GINI index. Thanks in advance</p>