text: Title: Handle all models with error
  Body: The AutoML crashes if all models have error. It should be handled more gently.
  
  The example of crash:
  ```
  AutoML directory: AutoML_88
  The task is multiclass_classification with evaluation metric logloss
  AutoML will use algorithms: ['MLP']
  AutoML steps: ['simple_algorithms', 'default_algorithms', 'not_so_random', 'hill_climbing_1', 'hill_climbing_2']
  Skip simple_algorithms because no parameters were generated.
  * Step default_algorithms will try to check up to 1 model
  The least populated class in y has only 2 members, which is less than n_splits=5.
  There was an error during 1_Default_MLP training.
  Please check AutoML_88/errors.md for details.
  * Step not_so_random will try to check up to 4 models
  There was an error during 1_MLP training.
  Please check AutoML_88/errors.md for details.
  There was an error during 2_MLP training.
  Please check AutoML_88/errors.md for details.
  There was an error during 3_MLP training.
  Please check AutoML_88/errors.md for details.
  There was an error during 4_MLP training.
  Please check AutoML_88/errors.md for details.
  Traceback (most recent call last):
    File "examples/scripts/nn_benchmark.py", line 53, in <module>
      mlp.fit(train_X, train_y)
    File "/home/piotr/sandbox/mljar-supervised/supervised/automl.py", line 276, in fit
      return self._fit(X, y)
    File "/home/piotr/sandbox/mljar-supervised/supervised/base_automl.py", line 723, in _fit
      raise e
    File "/home/piotr/sandbox/mljar-supervised/supervised/base_automl.py", line 672, in _fit
      step, self._models, self._results_path, self._stacked_models
    File "/home/piotr/sandbox/mljar-supervised/supervised/tuner/mljar_tuner.py", line 105, in generate_params
      return self.get_hill_climbing_params(models)
    File "/home/piotr/sandbox/mljar-supervised/supervised/tuner/mljar_tuner.py", line 335, in get_hill_climbing_params
      unique_model_types = np.unique(df_models.model_type)
    File "/home/piotr/sandbox/mljar-supervised/venv_mljs/lib/python3.6/site-packages/pandas/core/generic.py", line 5136, in __getattr__
      return object.__getattribute__(self, name)
  AttributeError: 'DataFrame' object has no attribute 'model_type'
  ```
  
  * the MLP algorithm in the example is experimental. It should be `Neural Network` used instead.