title: Why I got an TypeError: __init__() got an unexpected keyword argument 'outer_cv' in python sklearn?
body: <p>I have run the example code in the link: <a href="https://github.com/casperbh96/Nested-Cross-Validation/blob/master/Example%20Notebook%20-%20NestedCV.ipynb" rel="nofollow noreferrer">https://github.com/casperbh96/Nested-Cross-Validation/blob/master/Example%20Notebook%20-%20NestedCV.ipynb</a>, but one error was got:<strong>init</strong>() got an unexpected keyword argument 'outer_cv', I have check the source code, the 'outer_cv' was included in the <strong>int</strong>(), how to solve it? The example code also pasted as following:</p>
  
  <pre><code>from nested_cv import NestedCV
  
  import pandas as pd
  import numpy as np
  from sklearn.datasets import load_boston, load_iris, load_breast_cancer
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from sklearn.model_selection import KFold
  
  # When using Random Search, we get a user warning with this little number of hyperparameters
  # Suppress it
  import warnings
  warnings.simplefilter(action='ignore', category=UserWarning)
  warnings.simplefilter(action='ignore', category=FutureWarning)
  
  boston = load_boston()
  X = boston.data
  y = boston.target
  
  # Define a parameters grid
  param_grid = {
       'max_depth': [3, 7, 10, None],
       'n_estimators': [100,200],
       'min_samples_split':[2,3,5,7,10]
  }
  
  # Either specify a strategy or number
  # Here we choose a strategy
  outer_cv = KFold(n_splits=5,
                   shuffle=True,
                   random_state=123)
  inner_cv = KFold(n_splits=5,
                   shuffle=True,
                   random_state=123)
  
  NCV = NestedCV(model=RandomForestRegressor(), params_grid=param_grid,
                 outer_cv=outer_cv, inner_cv=inner_cv, n_jobs = -1,
                 cv_options={'sqrt_of_score':True, 
                             'recursive_feature_elimination':False, 
                             'rfe_n_features':2})
  NCV.fit(X=X,y=y)
  
  NCV.outer_scores
  </code></pre>