class_docstr: Test ConfusionMatrix visualizer
class_docstr_tok: ["Test","ConfusionMatrix","visualizer"]
docstr: Test filtering classes zooms in on the confusion matrix.
docstr_tok: ["Test","filtering","classes","zooms","in","on","the","confusion","matrix","."]
code: def test_class_filter_eg_zoom_in(self):
      """
      Test filtering classes zooms in on the confusion matrix.
      """
      _, ax = plt.subplots()
  
      model = LogisticRegression(random_state=93)
      cm = ConfusionMatrix(model, ax=ax, classes=[0, 1, 2])
      cm.fit(self.digits.X.train, self.digits.y.train)
      cm.score(self.digits.X.test, self.digits.y.test)
  
      self.assert_images_similar(cm, tol=10)
  
      # Ensure correct confusion matrix under the hood
      npt.assert_array_equal(
          cm.confusion_matrix_,
          np.array([[38, 0, 0], [0, 35, 0], [0, 0, 39]]),
      )
  
imports: import sys
  import pytest
  import yellowbrick
  import numpy.testing
  import matplotlib.pyplot
  from yellowbrick.exceptions import ModelError
  from yellowbrick.datasets import load_occupancy
  from yellowbrick.classifier.confusion_matrix import *
  from unittest.mock import patch
  from tests.fixtures import Dataset, Split
  from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase
  from sklearn.svm import SVC
  from sklearn.naive_bayes import GaussianNB
  from sklearn.preprocessing import LabelEncoder
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import PassiveAggressiveRegressor
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import load_digits, make_classification
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.pipeline import Pipeline
label_desc: Use image node_id 5 for calling the TestConfusionMatrix obj's underlying member method code with example usage: obj.test_class_filter_eg_zoom_in() without return types