class_docstr: ClassificationReport visualizer tests
class_docstr_tok: ["ClassificationReport","visualizer","tests"]
docstr: Correctly generates report for multi-class with LogisticRegression
docstr_tok: ["Correctly","generates","report","for","multi-class","with","LogisticRegression"]
code: def test_multiclass_class_report(self):
      """
      Correctly generates report for multi-class with LogisticRegression
      """
      _, ax = plt.subplots()
  
      viz = ClassificationReport(
          LogisticRegression(random_state=12), ax=ax
      )
      viz.fit(self.multiclass.X.train, self.multiclass.y.train)
      viz.score(self.multiclass.X.test, self.multiclass.y.test)
  
      self.assert_images_similar(viz, tol=11.0)
  
      assert viz.scores_ == {
          "precision": {
              0: 0.75,
              1: 0.47368421052631576,
              2: 0.45,
              3: 0.375,
              4: 0.5,
              5: 0.5294117647058824,
          },
          "recall": {
              0: 0.47368421052631576,
              1: 0.5625,
              2: 0.6428571428571429,
              3: 0.3157894736842105,
              4: 0.5,
              5: 0.5625,
          },
          "f1": {
              0: 0.5806451612903226,
              1: 0.5142857142857142,
              2: 0.5294117647058824,
              3: 0.34285714285714286,
              4: 0.5,
              5: 0.5454545454545455,
          },
      }
  
imports: import sys
  import pytest
  import yellowbrick
  import matplotlib.pyplot
  from yellowbrick.datasets import load_occupancy
  from yellowbrick.classifier.classification_report import *
  from pytest import approx
  from unittest.mock import patch
  from tests.base import VisualTestCase
  from sklearn.svm import LinearSVC, SVC
  from sklearn.naive_bayes import GaussianNB
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.datasets import make_classification
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LassoCV, LogisticRegression
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 2 for calling the TestClassificationReport obj's underlying member method code with example usage: obj.test_multiclass_class_report() without return types