class_docstr: Test the PredictionError visualizer
class_docstr_tok: ["Test","the","PredictionError","visualizer"]
docstr: Test that the user can supply an is_fitted param and it's state is maintained
docstr_tok: ["Test","that","the","user","can","supply","an","is_fitted","param","and","it","'s","state","is","maintained"]
code: def test_is_fitted_param(self):
      """
      Test that the user can supply an is_fitted param and it's state is maintained
      """
      # Instantiate a sklearn regressor
      model = Lasso(random_state=23, alpha=10)
      # Instantiate a prediction error plot, provide custom alpha
      visualizer = PredictionError(
          model, bestfit=False, identity=False, is_fitted=False
      )
  
      # Test param gets set correctly
      assert visualizer.is_fitted == False
  
imports: import pytest
  import matplotlib.pyplot
  import numpy
  from unittest import mock
  from tests.fixtures import Dataset, Split
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from yellowbrick.datasets import load_energy, load_concrete
  from yellowbrick.regressor.prediction_error import PredictionError, prediction_error
  from sklearn.datasets import make_regression
  from sklearn.linear_model import Ridge, Lasso
  from sklearn.neural_network import MLPRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.impute import SimpleImputer
label_desc: Use image node_id 9 for calling the TestPredictionError obj's underlying member method code with example usage: obj.test_is_fitted_param() without return types