text: Title: [BUG] GroupedPredictor doesn't allow for nans in input
  Body: The `GroupedPredictor` raises an error (`ValueError: Input contains NaN, infinity or a value too large for dtype('float64').`) even if the `estimator` can handle missing values itself.
  
  ```python
  from sklego.meta import GroupedPredictor
  import numpy as np
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LinearRegression
  from sklearn.impute import SimpleImputer
  from sklego.datasets import load_chicken
  
  df = load_chicken(as_frame=True)
  
  X, y = df.drop(columns='weight'),  df['weight']
  # create missing value
  X.loc[0, 'chick'] = np.nan
  model =  make_pipeline(SimpleImputer(), LinearRegression())
  group_model = GroupedPredictor(model, groups=["diet"])
  group_model.fit(X,y)
  ```
  
  This could be solved i.e. by exposing parameter `force_all_finite` from sklearn function `check_array` into the `GroupedPredictor`
  
  