class_docstr: Test the ClassificationScoreVisualizer base functionality
docstr: Test that a numeric y can be decoded using classes and encoder
docstr_tok: ["Test","that","a","numeric","y","can","be","decoded","using","classes","and","encoder"]
code: def test_decode_labels_from_numeric(self):
      """
      Test that a numeric y can be decoded using classes and encoder
      """
      classes = np.array(["a", "b", "c", "d", "e"])
      y = np.random.randint(0, 5, 100)
      decoded = classes[y]
  
      oz = ClassificationScoreVisualizer(GaussianNB, classes=classes)
      npt.assert_array_equal(oz._decode_labels(y), decoded)
  
      encoder = dict(zip(range(len(classes)), classes))
      oz = ClassificationScoreVisualizer(GaussianNB, encoder=encoder)
      npt.assert_array_equal(oz._decode_labels(y), decoded)
  
      encoder = LabelEncoder().fit(decoded)
      oz = ClassificationScoreVisualizer(GaussianNB, encoder=encoder)
      npt.assert_array_equal(oz._decode_labels(y), decoded)
  
imports: import pytest
  import numpy
  import numpy.testing
  from yellowbrick.classifier.base import *
  from sklearn.naive_bayes import GaussianNB
  from sklearn.preprocessing import LabelEncoder
  from sklearn.linear_model import LinearRegression
  from .conftest import assert_fitted, assert_not_fitted
  from yellowbrick.exceptions import YellowbrickTypeError
label_desc: Use image node_id 8 for calling the TestClassificationScoreVisualizer obj's underlying member method code with example usage: obj.test_decode_labels_from_numeric() without return types