class_docstr: Test ClassPredictionError visualizer
class_docstr_tok: ["Test","ClassPredictionError","visualizer"]
docstr: Test the ClassPredictionError quickmethod
docstr_tok: ["Test","the","ClassPredictionError","quickmethod"]
code: def test_class_prediction_error_quickmethod_X_test_and_y_test(self):
      """
      Test the ClassPredictionError quickmethod
      """
      X, y = load_occupancy(return_dataset=True).to_numpy()
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=42
      )
  
      fig = plt.figure()
      ax = fig.add_subplot()
  
      clf = SVC(random_state=42)
      viz = class_prediction_error(
          clf,
          X_train=X_train,
          y_train=y_train,
          X_test=X_test,
          y_test=y_test,
          ax=ax,
          show=False,
      )
  
      # Not sure why the tolerance must be so high for this
      # Failing on travis with RMS 9.544
      # AppVeyor and Linux conda fail due to non-text-based differences: RMS 12.961
      self.assert_images_similar(viz, tol=13, windows_tol=13)
  
imports: import pytest
  import matplotlib.pyplot
  from yellowbrick.exceptions import ModelError
  from yellowbrick.datasets import load_occupancy
  from yellowbrick.classifier.class_prediction_error import *
  from sklearn.svm import LinearSVC, SVC
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.datasets import make_multilabel_classification
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
  from unittest.mock import patch
  from tests.base import VisualTestCase
label_desc: Use image node_id 5 for calling the TestClassPredictionError obj's underlying member method code with example usage: obj.test_class_prediction_error_quickmethod_X_test_and_y_test() without return types