title: "ValueError: max_features must be in (0, n_features] " in scikit when using random forest
body: <p>I have a dataset of 20 features and 840 rows.
  I have already optimized the classifier (random forest). My parameters are n_estimators=100 and max_features=5.
  I want to do a classification for each feature. I mean with each of the features I want to know the prediction accuracy. But when I use my code I get an error.
  I am using scikit ver. 18.</p>
  
  <p>How can I fix the problem?</p>
  
  <pre><code>for name in ["AWA"]: 
      x=sio.loadmat('/home/TrainVal/{}_Fp1.mat'.format(name))['x'] 
      s_y=sio.loadmat('/home/TrainVal/{}_Fp1.mat'.format(name))['y']
      y=np.ravel(s_y)
  
      print(name, x.shape, y.shape) 
      print("")
  
  
      clf = make_pipeline(preprocessing.RobustScaler(), RandomForestClassifier(n_estimators = 100, 
                                                                           max_features=5, n_jobs=-1)) 
      #########10x10 SSS##############
      print("10x10")
  
      for i in range(x.shape[1]): 
          xA=x[:, i].reshape(-1,1)
  
          xSSSmean = [] 
          for j in range(10):
              sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=j) 
              scoresSSS = cross_val_score(clf, xA, y, cv=sss)
              xSSSmean.append(scoresSSS.mean()) 
  
          result_list.append(np.mean(xSSSmean))  
          plt.bar(i, np.mean(xSSSmean)*100, align = 'center')      
          plt.ylabel('Accuracy')
          plt.xlabel('Features')    
          plt.title('Accuracy per feature: {}_RF_Fp1(20)'.format(name)) 
  
          xticks=np.arange(i+1)
          plt.xticks(xticks, rotation = 'vertical')
      plt.show()
  
  
  
  
  
  #THE ERROR
  
  
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-2-a5faae7f83a2&gt; in &lt;module&gt;()
       24 
       25             sss = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=j)#ver18
  ---&gt; 26             scoresSSS = cross_val_score(clf, xA, y, cv=sss)
       27             xSSSmean.append(scoresSSS.mean()) 
       28             #print(scoresSSS)
  
   /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/model_selection/_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
      138                                               train, test, verbose, None,
      139                                               fit_params)
  --&gt; 140                       for train, test in cv_iter)
      141     return np.array(scores)[:, 0]
      142 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
      756             # was dispatched. In particular this covers the edge
      757             # case of Parallel used with an exhausted iterator.
  --&gt; 758             while self.dispatch_one_batch(iterator):
      759                 self._iterating = True
      760             else:
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
      606                 return False
      607             else:
  --&gt; 608                 self._dispatch(tasks)
      609                 return True
      610 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
      569         dispatch_timestamp = time.time()
      570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
  --&gt; 571         job = self._backend.apply_async(batch, callback=cb)
      572         self._jobs.append(job)
      573 
  
   /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
      107     def apply_async(self, func, callback=None):
      108         """Schedule a func to be run"""
  --&gt; 109         result = ImmediateResult(func)
      110         if callback:
      111             callback(result)
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
      324         # Don't delay the application, to avoid keeping the input
      325         # arguments in memory
  --&gt; 326         self.results = batch()
      327 
      328     def get(self):
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
   /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in &lt;listcomp&gt;(.0)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/model_selection/_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
      236             estimator.fit(X_train, **fit_params)
      237         else:
  --&gt; 238             estimator.fit(X_train, y_train, **fit_params)
      239 
      240     except Exception as e:
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
      268         Xt, fit_params = self._fit(X, y, **fit_params)
      269         if self._final_estimator is not None:
  --&gt; 270             self._final_estimator.fit(Xt, y, **fit_params)
      271         return self
      272 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)
      324                     t, self, X, y, sample_weight, i, len(trees),
      325                     verbose=self.verbose, class_weight=self.class_weight)
  --&gt; 326                 for i, t in enumerate(trees))
      327 
      328             # Collect newly grown trees
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
      756             # was dispatched. In particular this covers the edge
      757             # case of Parallel used with an exhausted iterator.
  --&gt; 758             while self.dispatch_one_batch(iterator):
      759                 self._iterating = True
      760             else:
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
      606                 return False
      607             else:
  --&gt; 608                 self._dispatch(tasks)
      609                 return True
      610 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
      569         dispatch_timestamp = time.time()
      570         cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
  --&gt; 571         job = self._backend.apply_async(batch, callback=cb)
      572         self._jobs.append(job)
      573 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
      107     def apply_async(self, func, callback=None):
      108         """Schedule a func to be run"""
  --&gt; 109         result = ImmediateResult(func)
      110         if callback:
      111             callback(result)
  
   /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
      324         # Don't delay the application, to avoid keeping the input
      325         # arguments in memory
  --&gt; 326         self.results = batch()
      327 
      328     def get(self):
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
   /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in &lt;listcomp&gt;(.0)
      129 
      130     def __call__(self):
  --&gt; 131         return [func(*args, **kwargs) for func, args, kwargs in self.items]
      132 
      133     def __len__(self):
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/ensemble/forest.py in _parallel_build_trees(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)
      118             curr_sample_weight *= compute_sample_weight('balanced', y, indices)
      119 
  --&gt; 120         tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)
      121     else:
      122         tree.fit(X, y, sample_weight=sample_weight, check_input=False)
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
      737             sample_weight=sample_weight,
      738             check_input=check_input,
  --&gt; 739             X_idx_sorted=X_idx_sorted)
      740         return self
      741 
  
  /home/karim/anaconda2/envs/scikit18/lib/python3.5/site-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
      244             raise ValueError("max_depth must be greater than zero. ")
      245         if not (0 &lt; max_features &lt;= self.n_features_):
  --&gt; 246             raise ValueError("max_features must be in (0, n_features]")
      247         if not isinstance(max_leaf_nodes, (numbers.Integral, np.integer)):
      248             raise ValueError("max_leaf_nodes must be integral number but was "
  
  ValueError: max_features must be in (0, n_features]
  </code></pre>