class_docstr: Simply wraps MAPLE into the common SHAP interface.
  
  Parameters
  ----------
  model : function
      User supplied function that takes a matrix of samples (# samples x # features) and
      computes the output of the model for those samples. The output can be a vector
      (# samples) or a matrix (# samples x # model outputs).
  
  data : numpy.array
      The background dataset.
code: def __init__(self, model, data):
      self.model = model
  
      if isinstance(data, pd.DataFrame):
          data = data.values
      self.data = data
      self.data_mean = self.data.mean(0)
  
      out = self.model(data)
      if len(out.shape) == 1:
          self.out_dim = 1
          self.flat_out = True
      else:
          self.out_dim = out.shape[1]
          self.flat_out = False
  
      X_train, X_valid, y_train, y_valid = train_test_split(
          data, out, test_size=0.2, random_state=0
      )
      self.explainer = MAPLE(X_train, y_train, X_valid, y_valid)
  
imports: import numpy
  import pandas
  from sklearn.model_selection import train_test_split
  from .._explainer import Explainer
  import numpy
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
  from sklearn.linear_model import Ridge
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 1 to create a new Maple object from inherited base classes: Explainer with example: obj = Maple(model, data)