code: def evaluate(model, X, y, cv):
      class SilentMetaStep(MetaStep):
          """This class is needed here to disable the sklearn compatibility errors with the getters and setters."""
  
          def __init__(self):
              super().__init__(wrapped=model)
  
          def get_params(self, deep=False) -> dict:
              return dict()
  
          def set_params(self, deep=False) -> dict:
              pass
  
      silent_model = SilentMetaStep()
      cv_results = cross_validate(
          silent_model,
          X,
          y,
          cv=cv,
          scoring=[
              "neg_mean_absolute_error",
              "neg_root_mean_squared_error",
          ],
      )
      mae = -cv_results["test_neg_mean_absolute_error"]
      rmse = -cv_results["test_neg_root_mean_squared_error"]
      print(
          f"Evaluation results for {model}:\n\n"
          f"    Mean Absolute Error:     {mae.mean():.3f} +/- {mae.std():.3f}\n"
          f"    Root Mean Squared Error: {rmse.mean():.3f} +/- {rmse.std():.3f}\n"
      )
  
file_path: Neuraxle/examples/sklearn/plot_cyclical_feature_engineering.py
imports: import matplotlib.pyplot
  import numpy
  import pandas
  from neuraxle.base import Identity, MetaStep
  from neuraxle.pipeline import Pipeline
  from neuraxle.steps.column_transformer import ColumnTransformer
  from neuraxle.union import FeatureUnion
  from sklearn.datasets import fetch_openml
  from sklearn.ensemble import HistGradientBoostingRegressor
  from sklearn.kernel_approximation import Nystroem
  from sklearn.linear_model import RidgeCV
  from sklearn.model_selection import TimeSeriesSplit, cross_validate
  from sklearn.preprocessing import FunctionTransformer, MinMaxScaler, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, SplineTransformer
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, X, y, cv) and returns: dict