code: def test_obvious_usecase(dataset, outlier_model):
      outlier_clf = OutlierClassifier(outlier_model)
      X = dataset
      y = (dataset.max(axis=1) > 3).astype(int)
      outlier_clf.fit(X, y)
      assert outlier_clf.predict([[10, 10]]) == np.array([1])
      assert outlier_clf.predict([[0, 0]]) == np.array([0])
      np.testing.assert_array_almost_equal(
          outlier_clf.predict_proba([[0, 0]]),
          np.array([[1, 0]]),
          decimal=3,
      )
      np.testing.assert_allclose(
          outlier_clf.predict_proba([[10, 10]]),
          np.array([[0, 1]]),
          atol=0.2,
      )
      assert isinstance(outlier_clf.score(X, y), float)
  
file_path: scikit-lego/tests/test_meta/test_outlier_classifier.py
imports: import numpy
  import pytest
  from sklearn.ensemble import IsolationForest
  from sklearn.linear_model import LinearRegression
  from sklearn.neighbors import LocalOutlierFactor
  from sklearn.svm import OneClassSVM
  from sklego.common import flatten
  from sklego.meta import OutlierClassifier
  from sklego.mixture import GMMOutlierDetector
  from tests.conftest import general_checks, select_tests
label_desc: Use image node_id 3 for calling a global function with example usage: test_obvious_usecase(dataset, outlier_model) without return types