text: Title: error 'numpy.float64' object is not iterable
  Body: How can I solve this error?
  ![화면 캡처 2023-06-21 144720](https://github.com/mljar/mljar-supervised/assets/43486924/9ea2f1ba-7eb2-43e3-b8ed-a1779daa5f7a)
  
  
  ```
  X_train, X_test, y_train, y_test = train_test_split(
      df[df.columns[1:-1]],
      df['Price'],
      test_size=0.25,
      random_state=123,
  )
  
  # train models with AutoML
  automl = AutoML(mode="Explain")
  automl.fit(X_train, y_train)
  
  # compute the MSE on test data
  predictions = automl.predict(X_test)
  print("Test MAE:", mean_absolute_error(y_test, predictions))
  ```
  
  
  These are output
  output:
  Linear algorithm was disabled.
  AutoML directory: AutoML_8
  The task is regression with evaluation metric rmse
  AutoML will use algorithms: ['Baseline', 'Decision Tree', 'Random Forest', 'Xgboost', 'Neural Network']
  AutoML will ensemble available models
  AutoML steps: ['simple_algorithms', 'default_algorithms', 'ensemble']
  * Step simple_algorithms will try to check up to 2 models
  1_Baseline rmse 35.216024 trained in 0.38 seconds
  Exception while producing SHAP explanations. module 'numpy' has no attribute 'bool'.
  `np.bool` was a deprecated alias for the builtin `bool`. To avoid this error in existing code, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.
  The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
      https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  Continuing ...
  2_DecisionTree rmse 19.749834 trained in 1.46 seconds
  * Step default_algorithms will try to check up to 3 models
  Exception while producing SHAP explanations. module 'numpy' has no attribute 'int'.
  `np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
  The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
      https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  Continuing ...
  3_Default_Xgboost rmse 9.812939 trained in 6.66 seconds
  There was an error during 3_Default_Xgboost training.
  Please check AutoML_8\errors.md for details.
  4_Default_NeuralNetwork rmse 13.374119 trained in 4.76 seconds
  There was an error during 4_Default_NeuralNetwork training.
  ...
  Ensemble rmse 9.812939 trained in 0.14 seconds
  AutoML fit time: 19.64 seconds
  AutoML best model: 3_Default_Xgboost
  Test MAE: 6.264868275003538
  
  
  
  And this is error.md
  ## Error for 3_Default_Xgboost
  
  'numpy.float64' object is not iterable
  Traceback (most recent call last):
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 1095, in _fit
      trained = self.train_model(params)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 386, in train_model
      mf.save(results_path, model_subpath)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in save
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in <listcomp>
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\preprocessing.py", line 582, in to_json
      preprocessing_params["scale_y"] = self._scale_y.to_json()
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\scale.py", line 76, in to_json
      data_json["X_min_values"] = list(self.X_min_values)
  TypeError: 'numpy.float64' object is not iterable
  
  
  Please set a GitHub issue with above error message at: https://github.com/mljar/mljar-supervised/issues/new
  
  ## Error for 4_Default_NeuralNetwork
  
  'numpy.float64' object is not iterable
  Traceback (most recent call last):
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 1095, in _fit
      trained = self.train_model(params)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 386, in train_model
      mf.save(results_path, model_subpath)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in save
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in <listcomp>
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\preprocessing.py", line 582, in to_json
      preprocessing_params["scale_y"] = self._scale_y.to_json()
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\scale.py", line 76, in to_json
      data_json["X_min_values"] = list(self.X_min_values)
  TypeError: 'numpy.float64' object is not iterable
  
  
  Please set a GitHub issue with above error message at: https://github.com/mljar/mljar-supervised/issues/new
  
  ## Error for 5_Default_RandomForest
  
  'numpy.float64' object is not iterable
  Traceback (most recent call last):
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 1095, in _fit
      trained = self.train_model(params)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\base_automl.py", line 386, in train_model
      mf.save(results_path, model_subpath)
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in save
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\model_framework.py", line 490, in <listcomp>
      preprocessing = [p.to_json() for p in self.preprocessings]
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\preprocessing.py", line 582, in to_json
      preprocessing_params["scale_y"] = self._scale_y.to_json()
    File "c:\Users\user\anaconda3\envs\mljar\lib\site-packages\supervised\preprocessing\scale.py", line 76, in to_json
      data_json["X_min_values"] = list(self.X_min_values)
  TypeError: 'numpy.float64' object is not iterable
  
  
  Please set a GitHub issue with above error message at: https://github.com/mljar/mljar-supervised/issues/new