title: Got continuous is not supported error in RandomForestRegressor
body: <p>I'm just trying to do a simple RandomForestRegressor example. But while testing the accuracy I get this error </p>
  
  <blockquote>
  <pre><code>/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc
  </code></pre>
    
    <p>in accuracy_score(y_true, y_pred, normalize, sample_weight)
            177 
            178     # Compute accuracy for each possible representation
        --> 179     y_type, y_true, y_pred = _check_targets(y_true, y_pred)
            180     if y_type.startswith('multilabel'):
            181         differing_labels = count_nonzero(y_true - y_pred, axis=1)</p>
  
  <pre><code>/Users/noppanit/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc
  </code></pre>
    
    <p>in _check_targets(y_true, y_pred)
             90     if (y_type not in ["binary", "multiclass", "multilabel-indicator",
             91                        "multilabel-sequences"]):
        ---> 92         raise ValueError("{0} is not supported".format(y_type))
             93 
             94     if y_type in ["binary", "multiclass"]:</p>
  
  <pre><code>ValueError: continuous is not supported
  </code></pre>
  </blockquote>
  
  <p>This is the sample of the data. I can't show the real data. </p>
  
  <pre><code>target, func_1, func_2, func_2, ... func_200
  float, float, float, float, ... float
  </code></pre>
  
  <p>Here's my code.</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  from sklearn.preprocessing import Imputer
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor
  from sklearn.cross_validation import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn import tree
  
  train = pd.read_csv('data.txt', sep='\t')
  
  labels = train.target
  train.drop('target', axis=1, inplace=True)
  cat = ['cat']
  train_cat = pd.get_dummies(train[cat])
  
  train.drop(train[cat], axis=1, inplace=True)
  train = np.hstack((train, train_cat))
  
  imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
  imp.fit(train)
  train = imp.transform(train)
  
  x_train, x_test, y_train, y_test = train_test_split(train, labels.values, test_size = 0.2)
  
  clf = RandomForestRegressor(n_estimators=10)
  
  clf.fit(x_train, y_train)
  y_pred = clf.predict(x_test)
  accuracy_score(y_test, y_pred) # This is where I get the error.
  </code></pre>