code: def build_ensemble(**kwargs):
      """Generate ensemble."""
  
      ens = SuperLearner(**kwargs)
      prep = {
          "Standard Scaling": [StandardScaler()],
          "Min Max Scaling": [MinMaxScaler()],
          "No Preprocessing": [],
      }
  
      est = {
          "Standard Scaling": [
              ElasticNet(),
              Lasso(),
              KNeighborsRegressor(),
          ],
          "Min Max Scaling": [SVR()],
          "No Preprocessing": [
              RandomForestRegressor(random_state=SEED),
              GradientBoostingRegressor(),
          ],
      }
  
      ens.add(est, prep)
  
      ens.add(GradientBoostingRegressor(), meta=True)
  
      return ens
  
file_path: mlens/benchmarks/friedman_scores.py
imports: import numpy
  import os
  from mlens.ensemble import SuperLearner
  from mlens.utils import safe_print
  from mlens.metrics import rmse
  from sklearn.datasets import make_friedman1
  from sklearn.base import clone
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import Lasso, ElasticNet
  from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
  from sklearn.svm import SVR
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.preprocessing import StandardScaler, MinMaxScaler
  from sklearn.kernel_ridge import KernelRidge
  from time import time
label_desc: Use image node_id 1 for calling a global function with example usage: build_ensemble() and returns: ens