text: Title: Can Autosklearn handle Multi-Class/Multi-Label Classification and which classifiers will it use?
  Body: I have been trying to use AutoSklearn with Multi-class classification
  
  so my labels are like this
  
  0   1   2    3    4 ...  200
  1   0   1   1     1 ...   1
  0   1   0   0     1 ...   0
  1   0   0   1     0 ...   0
  1   1   0   1     0 ...   1
  0   1   1   0     1 ...   0
  1   1   1   0     0 ...   1
  1   0   1   0     1 ...   0
  
  
  
  
  I used this code
  
  ```python
  y = y[:, (65,67,54,133,122,63,102,105,39)]
  X = df.drop(Code, axis=1, errors='ignore')
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
  
  automl = autosklearn.classification.AutoSklearnClassifier(
  include={'feature_preprocessor': ["no_preprocessing"], 
   },
  exclude={ 'classifier': ['random_forest']},
  time_left_for_this_task=60*5,
  per_run_time_limit=60*1,
  memory_limit = 1024 * 10,
  n_jobs=-1,
  metric=autosklearn.metrics.f1_macro,
          )
  
  ```
  
  but now  I want to train Autosklearn on Multi-class Multi-label classification
  
  Which method of these shall i use?
  
  
  **1-** 
  
  ```
  clf = OneVsRestClassifier(automl, n_jobs=-1)
  clf.fit(X_train, y_train)
  
  ```
  
  **2-** 
  ```
  
  clf = automl
  clf.fit(X_train, y_train)
  ```
  
  **3-**
  
  I have to loop one class at a time and use 
  
  ```
  clf = automl
  clf.fit(X_train, y_train)
  ```
  
  
  so it will be like 
  
  ```
  for i in (65,67,54,133,122,63,102,105,39):
         y = z[:, i]
         X = df.drop(Code, axis=1, errors='ignore')
         X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        automl = autosklearn.classification.AutoSklearnClassifier(
        include={'feature_preprocessor': ["no_preprocessing"], 
         },
        exclude={ 'classifier': ['random_forest']},
        time_left_for_this_task=60*5,
        per_run_time_limit=60*1,
        memory_limit = 1024 * 10,
        n_jobs=1,
        metric=autosklearn.metrics.f1_macro,
                )
  
  
        clf = automl
        clf.fit(X_train, y_train)
  ```
  
  so I get a different model for each label?
  
  