def get_support(self, indices=False):
        """Get a mask, or integer index, of the samples excluded, i.e, samples
        detected as outliers.

        Parameters
        ----------
        indices : boolean (default False)
            If True, the return value will be an array of integers, rather
            than a boolean mask.

        Returns
        -------
        support : array, shape = (n_samples)
            An index that selects the excluded samples from a vector.
            If `indices` is False, this is a boolean array, in which an element
            is True iff its corresponding sample is excluded. If `indices` is
            True, this is an integer array whose values are indices into the
            input vector.
        """
        if not self._is_fitted:
            raise NotFittedError("This {} instance is not fitted yet. Call "
                                 "'fit' with appropriate arguments."
                                 .format(self.__class__.__name__))

        mask = self._support
        return mask if not indices else np.where(mask)[0]
Get a mask, or integer index, of the samples excluded, i.e, samples
        detected as outliers.

        Parameters
        ----------
        indices : boolean (default False)
            If True, the return value will be an array of integers, rather
            than a boolean mask.

        Returns
        -------
        support : array, shape = (n_samples)
            An index that selects the excluded samples from a vector.
            If `indices` is False, this is a boolean array, in which an element
            is True iff its corresponding sample is excluded. If `indices` is
            True, this is an integer array whose values are indices into the
            input vector.
        
optbinning/binning/outlier.py