class_docstr: Test the PredictionError visualizer
class_docstr_tok: ["Test","the","PredictionError","visualizer"]
docstr: Test NumPy real world dataset with image similarity on Ridge
docstr_tok: ["Test","NumPy","real","world","dataset","with","image","similarity","on","Ridge"]
code: def test_prediction_error_numpy(self):
      """
      Test NumPy real world dataset with image similarity on Ridge
      """
      _, ax = plt.subplots()
  
      # Load the occupancy dataset from fixtures
      data = load_energy(return_dataset=True)
      X, y = data.to_numpy()
  
      # Create train/test splits
      splits = tts(X, y, test_size=0.2, random_state=8873)
      X_train, X_test, y_train, y_test = splits
  
      visualizer = PredictionError(Ridge(random_state=22), ax=ax)
      visualizer.fit(X_train, y_train)
      visualizer.score(X_test, y_test)
      visualizer.finalize()
  
      self.assert_images_similar(visualizer, tol=1, remove_legend=True)
  
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 3 for calling the TestPredictionError obj's underlying member method code with example usage: obj.test_prediction_error_numpy() without return types