class_docstr: Morphs a regression estimator into one that can detect outliers. We will try to predict `column` in X.
  
  Parameters
  ----------
  model : scikit-learn compatible regression model
      A regression model that will be used for prediction.
  column : int
      The index of the target column to predict in the input data.
  lower : float, default=2.0
      Lower threshold for outlier detection. The method used for detection depends on the `method` parameter.
  upper : float, default=2.0
      Upper threshold for outlier detection. The method used for detection depends on the `method` parameter.
  method : Literal["sd", "relative", "absolute"], default="sd"
      The method to use for outlier detection.
  
      - `"sd"` uses standard deviation
      - `"relative"` uses relative difference
      - `"absolute"` uses absolute difference
  
  Attributes
  ----------
  estimator_ : scikit-learn compatible regression model
      The fitted underlying regression model.
  sd_ : float
      The standard deviation of the differences between true and predicted values.
  idx_ : int
      The index of the target column in the input data.
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docstr: Check if the underlying model is a regression model.
docstr_tok: ["Check","if","the","underlying","model","is","a","regression","model","."]
code: def _is_regression_model(self):
      """Check if the underlying model is a regression model."""
      return any(
          [
              "RegressorMixin" in p.__name__
              for p in type(self.model).__bases__
          ]
      )
  
file_path: scikit-lego/sklego/meta/regression_outlier_detector.py
imports: import numpy
  import pandas
  from sklearn.base import BaseEstimator, OutlierMixin
  from sklearn.utils.validation import check_array, check_is_fitted
label_desc: Use image node_id 2 for calling the RegressionOutlierDetector obj's underlying member method code with example usage: obj._is_regression_model() and returns: any