class_docstr: ClassificationReport visualizer tests
class_docstr_tok: ["ClassificationReport","visualizer","tests"]
docstr: Correctly generates a report showing support as a raw count
docstr_tok: ["Correctly","generates","a","report","showing","support","as","a","raw","count"]
code: def test_support_count_class_report(self):
      """
      Correctly generates a report showing support as a raw count
      """
      _, ax = plt.subplots()
  
      viz = ClassificationReport(
          LinearSVC(random_state=42), ax=ax, support="count"
      )
      viz.fit(self.binary.X.train, self.binary.y.train)
      viz.score(self.binary.X.test, self.binary.y.test)
  
      self.assert_images_similar(viz, tol=40)
  
      assert viz.scores_ == {
          "precision": {0: approx(0.7446808), 1: approx(0.8490566)},
          "recall": {0: approx(0.8139534), 1: approx(0.7894736)},
          "f1": {0: approx(0.7777777), 1: approx(0.8181818)},
          "support": {
              0: approx(0.42999999999999999),
              1: approx(0.56999999999999995),
          },
      }
  
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 7 for calling the TestClassificationReport obj's underlying member method code with example usage: obj.test_support_count_class_report() without return types