title: Transform from one decision tree (J48) classification to ensemble in python
body: <p>I would like to implement the classification of the algorithm based on the <a href="https://www.sciencedirect.com/science/article/pii/S156625350400034X?via%3Dihub" rel="nofollow noreferrer">paper</a>. I have a single <code>J48</code> (C4.5) decision tree (code mentioned down). I would like to run it for several (<code>I_max</code>) times over the dataset and calculate the C* = class membership probabilities for all the ensemble. As described here and in page 8 in the paper. <a href="https://i.stack.imgur.com/16zEz.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/16zEz.png" alt="enter image description here"></a></p>
  
  <pre><code>import numpy as np
  import pandas as pd
  from sklearn.cross_validation import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.metrics import accuracy_score
  from sklearn import tree
  url="https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data"
  c=pd.read_csv(url, header=None)
  X = c.values[:,1:8]
  Y = c.values[:,0]
  X_train, X_test, y_train, y_test = train_test_split( X, Y, test_size = 0.3, random_state = 100)
  clf_entropy = DecisionTreeClassifier(criterion = "entropy", random_state = 100,
   max_depth=3, min_samples_leaf=5)
  clf_entropy.fit(X_train, y_train)
  probs = clf_entropy.predict_proba(X_test)
  probs
  </code></pre>