class_docstr: A class to pre-process targets.
  
  It validates the data provided during fit to make sure it matches Sklearn
  expectation as well as encoding the targets in case of classification
  
  Attributes
  ----------
  is_classification: bool
      A bool that indicates if the validator should operate in classification mode.
      During classification, the targets are encoded.
  
  encoder: Optional[BaseEstimator]
      Host a encoder object if the data requires transformation (for example, if
      provided a categorical column in a pandas DataFrame).
  
  enc_columns: List[str]
      List of columns that where encoded
class_docstr_tok: ["A","class","to","pre-process","targets",".","It","validates","the","data","provided","during","fit","to","make","sure","it","matches","Sklearn","expectation","as","well","as","encoding","the","targets","in","case","of","classification","Attributes","--","--","--","--","--","is_classification",":","bool","A","bool","that","indicates","if","the","validator","should","operate","in","classification","mode",".","During","classification",",","the","targets","are","encoded",".","encoder",":","Optional","[","BaseEstimator","]","Host","a","encoder","object","if","the","data","requires","transformation","(","for","example",",","if","provided","a","categorical","column","in","a","pandas","DataFrame",")",".","enc_columns",":","List","[","str","]","List","of","columns","that","where","encoded"]
docstr: Output is encoded with a single column encoding
docstr_tok: ["Output","is","encoded","with","a","single","column","encoding"]
code: def is_single_column_target(self) -> bool:
      """Output is encoded with a single column encoding"""
      return self.out_dimensionality == 1
  
imports: from typing import List, Optional, Type, Union, cast
  import logging
  import warnings
  import numpy
  import pandas
  import sklearn.utils
  from pandas.api.types import is_numeric_dtype
  from pandas.core.dtypes.base import ExtensionDtype
  from scipy.sparse import spmatrix
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.utils.multiclass import type_of_target
  from autosklearn.util.logging_ import PickableLoggerAdapter
label_desc: Use image node_id 6 for calling the TargetValidator obj's underlying member method code with example usage: obj.is_single_column_target() and returns: unknown