class_docstr: Test the LearningCurve visualizer
docstr: Test image closeness on a classification dataset
docstr_tok: ["Test","image","closeness","on","a","classification","dataset"]
code: def test_classifier(self):
      """
      Test image closeness on a classification dataset
      """
      X, y = self.classification
  
      oz = LearningCurve(
          RandomForestClassifier(random_state=21), random_state=12
      ).fit(X, y)
      oz.finalize()
  
      self.assert_images_similar(oz, tol=0.1)
  
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 2 for calling the TestLearningCurve obj's underlying member method code with example usage: obj.test_classifier() without return types