class_docstr: Meta-transformer for selecting categorical features and transform them using OneHotEncoder.
  
  Parameters
  ----------
  
  threshold : int, default=10
      Maximum number of unique values per feature to consider the feature
      to be categorical.
  
  minimum_fraction: float, default=None
      Minimum fraction of unique values in a feature to consider the feature
      to be categorical.
class_docstr_tok: ["Meta-transformer","for","selecting","categorical","features","and","transform","them","using","OneHotEncoder",".","Parameters","--","--","--","--","--","threshold",":","int",",","default=10","Maximum","number","of","unique","values","per","feature","to","consider","the","feature","to","be","categorical",".","minimum_fraction",":","float",",","default=None","Minimum","fraction","of","unique","values","in","a","feature","to","consider","the","feature","to","be","categorical","."]
docstr: Create a CategoricalSelector object.
docstr_tok: ["Create","a","CategoricalSelector","object","."]
code: def __init__(self, threshold=10, minimum_fraction=None):
      """Create a CategoricalSelector object."""
      self.threshold = threshold
      self.minimum_fraction = minimum_fraction
  
file_path: tpot/tpot/builtins/feature_transformers.py
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.decomposition import PCA
  from .one_hot_encoder import OneHotEncoder, auto_select_categorical_features, _X_selected
label_desc: Use image node_id 1 to create a new CategoricalSelector object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = CategoricalSelector(threshold, minimum_fraction)