class_docstr: Test ClassPredictionError visualizer
class_docstr_tok: ["Test","ClassPredictionError","visualizer"]
docstr: Test that visualizer can handle sklearn pipeline as model input
docstr_tok: ["Test","that","visualizer","can","handle","sklearn","pipeline","as","model","input"]
code: def test_pipeline_as_model_input(self):
      """
      Test that visualizer can handle sklearn pipeline as model input
      """
      X, y = load_occupancy(return_dataset=True).to_pandas()
      classes = ["unoccupied", "occupied"]
  
      X_train, X_test, y_train, y_test = tts(
          X, y, test_size=0.2, shuffle=True, random_state=42
      )
  
      model = Pipeline(
          [("minmax", MinMaxScaler()), ("svc", SVC(random_state=42))]
      )
  
      oz = ClassPredictionError(model, classes=classes)
      oz.fit(X_train, y_train)
      oz.score(X_test, y_test)
      oz.finalize()
      self.assert_images_similar(oz, tol=12.5, windows_tol=13.3)
  
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 14 for calling the TestClassPredictionError obj's underlying member method code with example usage: obj.test_pipeline_as_model_input() without return types