code: def fit_select_best(X, y):
      """
      Selects the best fit of the estimators already implemented by choosing the
      model with the smallest mean square error metric for the trained values.
      """
      models = [fit(X, y) for fit in [fit_linear, fit_quadratic]]
      errors = map(lambda model: mse(y, model.predict(X)), models)
  
      return min(zip(models, errors), key=itemgetter(1))[0]
  
file_path: yellowbrick/yellowbrick/bestfit.py
imports: import numpy
  import matplotlib.pyplot
  from sklearn import linear_model
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.pipeline import make_pipeline
  from sklearn.metrics import mean_squared_error
  from operator import itemgetter
  from yellowbrick.style.palettes import LINE_COLOR
  from yellowbrick.exceptions import YellowbrickValueError
label_desc: Use image node_id 2 for calling a global function with example usage: fit_select_best(X, y) and returns: min