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: Fit the SelectByMinCount model.
  
  Parameters
  ----------
  X : numpy.ndarray, with shape (n_samples, n_features)
      The training data to fit.
  y : Ignored
      Not used.
  
  Returns
  -------
  self
docstr_tok: ["Fit","the","SelectByMinCount","model",".","Parameters","--","--","--","--","--","X",":","numpy.ndarray",",","with","shape","(","n_samples",",","n_features",")","The","training","data","to","fit",".","y",":","Ignored","Not","used",".","Returns","--","--","--","-","self"]
code: def fit(self, X, y=None):
      """
      Fit the SelectByMinCount model.
  
      Parameters
      ----------
      X : numpy.ndarray, with shape (n_samples, n_features)
          The training data to fit.
      y : Ignored
          Not used.
  
      Returns
      -------
      self
      """
      # initialize a list of counts of times each feature appears
      col_counts = [0 for _ in range(X.shape[1])]
  
      if sp.issparse(X):
          # find() is scipy.sparse's equivalent of nonzero()
          _, col_indices, _ = sp.find(X)
      else:
          # assume it's a numpy array (not a numpy matrix)
          col_indices = X.nonzero()[1].tolist()
  
      for i in col_indices:
          col_counts[i] += 1
  
      self.scores_ = np.array(col_counts)
      return self
  
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 2 for calling the SelectByMinCount obj's underlying member method code with example usage: obj.fit(X, y) and returns: self