class_docstr: Manages issues related to underperforming group examples.
  
  Note: The `min_cluster_samples` argument should not be confused with the
  `min_samples` argument of sklearn.cluster.DBSCAN.
  
  Examples
  --------
  >>> from cleanlab import Datalab
  >>> import numpy as np
  >>> X = np.random.normal(size=(50, 2))
  >>> y = np.random.randint(2, size=50)
  >>> pred_probs = X / X.sum(axis=1, keepdims=True)
  >>> data = {"X": X, "y": y}
  >>> lab = Datalab(data, label_name="y")
  >>> issue_types={"underperforming_group": {"clustering_kwargs": {"eps": 0.5}}}
  >>> lab.find_issues(pred_probs=pred_probs, features=X, issue_types=issue_types)
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code: def __init__(
      self,
      datalab: Datalab,
      metric: Optional[str] = None,
      threshold: float = 0.1,
      k: int = 10,
      clustering_kwargs: Dict[str, Any] = {},
      min_cluster_samples: int = 5,
      **_: Any,
  ):
      super().__init__(datalab)
      self.metric = metric
      self.threshold = self._set_threshold(threshold)
      self.k = k
      self.clustering_kwargs = clustering_kwargs
      self.min_cluster_samples = min_cluster_samples
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union, Tuple
  import warnings
  import inspect
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from sklearn.neighbors import NearestNeighbors
  from sklearn.exceptions import NotFittedError
  from sklearn.utils.validation import check_is_fitted
  from sklearn.cluster import DBSCAN
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.rank import get_self_confidence_for_each_label
label_desc: Use image node_id 1 to create a new UnderperformingGroupIssueManager object from inherited base classes: IssueManager with example: obj = UnderperformingGroupIssueManager(datalab, metric, threshold, k, clustering_kwargs, min_cluster_samples)