class_docstr: Base class for sklearn backend outlier detection algorithms.
class_docstr_tok: ["Base","class","for","sklearn","backend","outlier","detection","algorithms","."]
docstr: Classify outliers.
  
  Parameters
  ----------
  x
      Data to classify.
docstr_tok: ["Classify","outliers",".","Parameters","--","--","--","--","--","x","Data","to","classify","."]
code: def __call__(self, x: np.ndarray) -> np.ndarray:
      """Classify outliers.
  
      Parameters
      ----------
      x
          Data to classify.
      """
      scores = self.score(x)
      self.check_threshold_inferred()
      return self._classify_outlier(scores)
  
imports: from typing import List, Union, Optional, Dict
  from dataclasses import dataclass, asdict
  from abc import ABC, abstractmethod
  from typing_extensions import Self
  import numpy
  from alibi_detect.exceptions import NotFittedError, ThresholdNotInferredError
label_desc: Use image node_id 9 for calling the SklearnOutlierDetector obj's underlying member method code with example usage: obj.__call__(x) and returns: self