class_docstr: Select or discard features based on how often they occur in the data.
  
  Select features occurring in more (and/or fewer than) than a specified
  number of examples in the training data (or a CV training fold).
  
  Parameters
  ----------
  min_count : int, default=1
      The minimum feature count to select.
docstr: Return a mask indicating which features to keep.
  
  Adapted from ``SelectKBest``.
  
  Returns
  -------
  mask : numpy.ndarray
      The mask with features to keep set to True.
docstr_tok: ["Return","a","mask","indicating","which","features","to","keep",".","Adapted","from","``","SelectKBest","``",".","Returns","--","--","--","-","mask",":","numpy.ndarray","The","mask","with","features","to","keep","set","to","True","."]
code: def _get_support_mask(self):
      """
      Return a mask indicating which features to keep.
  
      Adapted from ``SelectKBest``.
  
      Returns
      -------
      mask : numpy.ndarray
          The mask with features to keep set to True.
      """
      mask = np.zeros(self.scores_.shape, dtype=bool)
      mask[self.scores_ >= self.min_count] = True
      return mask
  
imports: from __future__ import annotations
  import inspect
  import logging
  import sys
  import time
  from collections import Counter, defaultdict
  from csv import DictWriter, excel_tab
  from functools import wraps
  from importlib import import_module
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
  import joblib
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_selection import SelectKBest
  from sklearn.metrics import accuracy_score, confusion_matrix, precision_recall_fscore_support
  from sklearn.model_selection import KFold, LeaveOneGroupOut, ShuffleSplit, StratifiedKFold
  from skll.data import FeatureSet
  from skll.metrics import _CUSTOM_METRICS, _PREDEFINED_CUSTOM_METRICS, use_score_func
  from skll.types import ComputeEvalMetricsResults, ConfusionMatrix, FeaturesetIterator, FoldMapping, IdType, IndexIterator, LabelType, PathOrStr, SparseFeatureMatrix
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS, CORRELATION_METRICS, REGRESSION_ONLY_METRICS, UNWEIGHTED_KAPPA_METRICS, WEIGHTED_KAPPA_METRICS
  from skll.version import VERSION
label_desc: Use image node_id 3 for calling the SelectByMinCount obj's underlying member method code with example usage: obj._get_support_mask() and returns: mask