class_docstr: Test the LearningCurve visualizer
docstr: Test the learning curve quick method acts as expected
docstr_tok: ["Test","the","learning","curve","quick","method","acts","as","expected"]
code: def test_quick_method(self):
      """
      Test the learning curve quick method acts as expected
      """
      X, y = self.classification
      train_sizes = np.linspace(0.1, 1.0, 8)
      viz = learning_curve(
          GaussianNB(),
          X,
          y,
          train_sizes=train_sizes,
          cv=ShuffleSplit(n_splits=10, test_size=0.2, random_state=34),
          scoring="f1_macro",
          random_state=43,
          show=False,
      )
  
      self.assert_images_similar(viz)
  
imports: import sys
  import pytest
  import numpy
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import LinearSVC
  from sklearn.linear_model import Ridge
  from sklearn.naive_bayes import GaussianNB, MultinomialNB
  from sklearn.cluster import MiniBatchKMeans
  from sklearn.preprocessing import OneHotEncoder, LabelEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.model_selection import ShuffleSplit
  from sklearn.model_selection import StratifiedKFold
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from yellowbrick.datasets import load_mushroom, load_game
  from yellowbrick.model_selection.learning_curve import *
label_desc: Use image node_id 5 for calling the TestLearningCurve obj's underlying member method code with example usage: obj.test_quick_method() without return types