class_docstr: Morphs an outlier detection model into a classifier.
  
  When an outlier is detected it will output 1 and 0 otherwise. This way you can use familiar metrics again and
  this allows you to consider outlier models as a fraud detector.
  
  Parameters
  ----------
  model : scikit-learn compatible outlier detection model
      An outlier detection model that will be used for prediction.
  
  Attributes
  ----------
  estimator_ : scikit-learn compatible outlier detection model
      The fitted underlying outlier detection model.
  classes_ : array-like of shape (2,)
      Classes used for prediction (0 or 1)
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docstr: Predict probability estimates for new data.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      The data to predict.
  
  Returns
  -------
  np.ndarray of shape (n_samples,)
      The predicted probabilities.
docstr_tok: ["Predict","probability","estimates","for","new","data",".","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","The","data","to","predict",".","Returns","--","--","--","-","np.ndarray","of","shape","(","n_samples",",",")","The","predicted","probabilities","."]
code: def predict_proba(self, X):
      """Predict probability estimates for new data.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          The data to predict.
  
      Returns
      -------
      np.ndarray of shape (n_samples,)
          The predicted probabilities.
      """
      check_is_fitted(self, ["estimator_", "classes_"])
      decision_function_scores = self.estimator_.decision_function(X)
      probabilities = self._predict_proba_sigmoid.predict(
          decision_function_scores
      ).reshape(-1, 1)
      complement = np.ones_like(probabilities) - probabilities
      return np.hstack((complement, probabilities))
  
file_path: scikit-lego/sklego/meta/outlier_classifier.py
imports: import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.calibration import _SigmoidCalibration
  from sklearn.utils.validation import check_is_fitted, check_X_y
  from sklego.base import OutlierModel
label_desc: Use image node_id 5 for calling the OutlierClassifier obj's underlying member method code with example usage: obj.predict_proba(X) and returns: np