title: Why is Random Forest with a single tree much better than a Decision Tree classifier?
body: <p>I apply the
  decision tree classifier and the random forest classifier to my data with the following code:</p>
  
  <pre class="lang-python prettyprint-override"><code>def decision_tree(train_X, train_Y, test_X, test_Y):
  
      clf = tree.DecisionTreeClassifier()
      clf.fit(train_X, train_Y)
  
      return clf.score(test_X, test_Y)
  
  
  def random_forest(train_X, train_Y, test_X, test_Y):
      clf = RandomForestClassifier(n_estimators=1)
      clf = clf.fit(X, Y)
  
      return clf.score(test_X, test_Y)
  </code></pre>
  <p>Why the result are so much better for the random forest classifier (for 100 runs, with randomly sampling 2/3 of data for the training and 1/3 for the test)?</p>
  <pre class="lang-python prettyprint-override"><code>100%|███████████████████████████████████████| 100/100 [00:01&lt;00:00, 73.59it/s]
  Algorithm: Decision Tree
    Min     : 0.3883495145631068
    Max     : 0.6476190476190476
    Mean    : 0.4861783113770316
    Median  : 0.48868030937802126
    Stdev   : 0.047158171852401135
    Variance: 0.0022238931724605985
  100%|███████████████████████████████████████| 100/100 [00:01&lt;00:00, 85.38it/s]
  Algorithm: Random Forest
    Min     : 0.6846846846846847
    Max     : 0.8653846153846154
    Mean    : 0.7894823428836184
    Median  : 0.7906101571063208
    Stdev   : 0.03231671150915106
    Variance: 0.0010443698427656967
  </code></pre>
  <p>The random forest estimators with one estimator isn't just a decision tree?
  Have i done something wrong or misunderstood the concept?</p>