class_docstr: Base class for sklearn backend outlier detection algorithms.
class_docstr_tok: ["Base","class","for","sklearn","backend","outlier","detection","algorithms","."]
docstr: Classify the data as outlier or not.
  
  Parameters
  ----------
  scores
      Scores to classify. Larger scores indicate more likely outliers.
  
  Returns
  -------
  `np.ndarray` or ``None``
docstr_tok: ["Classify","the","data","as","outlier","or","not",".","Parameters","--","--","--","--","--","scores","Scores","to","classify",".","Larger","scores","indicate","more","likely","outliers",".","Returns","--","--","--","-","`","np.ndarray","`","or","``","None","``"]
code: def _classify_outlier(
      self, scores: np.ndarray
  ) -> Optional[np.ndarray]:
      """Classify the data as outlier or not.
  
      Parameters
      ----------
      scores
          Scores to classify. Larger scores indicate more likely outliers.
  
      Returns
      -------
      `np.ndarray` or ``None``
      """
      if self.threshold_inferred and self.threshold is not None:
          return (scores > self.threshold).astype(int)
      return None
  
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 5 for calling the SklearnOutlierDetector obj's underlying member method code with example usage: obj._classify_outlier(scores) and returns: None, unknown