title: Using the predict_proba() function of RandomForestClassifier in the safe and right way
body: <p>I'm using Scikit-learn. Sometimes I need to have the probabilities of labels/classes instead of the labels/classes themselves. Instead of having Spam/Not Spam as labels of emails, I wish to have only for example: 0.78 probability a given email is Spam.</p>
  <p>For such purpose, I'm using <code>predict_proba()</code> with RandomForestClassifier as following:</p>
  <pre><code>clf = RandomForestClassifier(n_estimators=10, max_depth=None,
      min_samples_split=1, random_state=0)
  scores = cross_val_score(clf, X, y)
  print(scores.mean())
  
  classifier = clf.fit(X,y)
  predictions = classifier.predict_proba(Xtest)
  print(predictions)
  </code></pre>
  <p>And I got those results:</p>
  <pre><code> [ 0.4  0.6]
   [ 0.1  0.9]
   [ 0.2  0.8]
   [ 0.7  0.3]
   [ 0.3  0.7]
   [ 0.3  0.7]
   [ 0.7  0.3]
   [ 0.4  0.6]
  </code></pre>
  <p>Where the second column is for class: Spam. However, I have two main issues with the results about which I am not confident. The first issue is that the results represent the probabilities of the labels without being affected by the size of my data? The second issue is that the results show only one digit which is not very specific in some cases where the 0.701 probability is very different from 0.708. Is there any way to get the next 5 digit for example?</p>