class_docstr: Base class providing common functions for multi-label classifiers
  that follow the problem transformation approach.
  
  Problem transformation is the approach in which the
  original multi-label classification problem is transformed into one
  or more single-label problems, which are then solved by single-class
  or multi-class classifiers.
  
  Scikit-multilearn provides a number of such methods:
  
  - :class:`BinaryRelevance` - performs a single-label single-class classification for each label and sums the results :class:`BinaryRelevance`
  - :class:`ClassifierChains` - performs a single-label single-class classification for each label and sums the results :class:`ClassifierChain`
  - :class:`LabelPowerset` - performs a single-label single-class classification for each label and sums the results :class:`LabelPowerset`
  
  Parameters
  ----------
  classifier : scikit classifier type
      The base classifier that will be used in a class, will be automagically put under self.classifier for future access.
  require_dense : boolean (default is False)
      Whether the base classifier requires input as dense arrays.
code: def __init__(self, classifier=None, require_dense=None):
      super(ProblemTransformationBase, self).__init__()
  
      self.copyable_attrs = ["classifier", "require_dense"]
  
      self.classifier = classifier
      if require_dense is not None:
          if isinstance(require_dense, bool):
              self.require_dense = [require_dense, require_dense]
          else:
              assert (
                  len(require_dense) == 2
                  and isinstance(require_dense[0], bool)
                  and isinstance(require_dense[1], bool)
              )
              self.require_dense = require_dense
  
      else:
          if isinstance(self.classifier, MLClassifierBase):
              self.require_dense = [False, False]
          else:
              self.require_dense = [True, True]
  
file_path: scikit-multilearn/skmultilearn/base/problem_transformation.py
imports: import numpy
  from .base import MLClassifierBase
  from ..utils import matrix_creation_function_for_format
  from scipy.sparse import issparse, csr_matrix
label_desc: Use image node_id 1 to create a new ProblemTransformationBase object from inherited base classes: MLClassifierBase with example: obj = ProblemTransformationBase(classifier, require_dense)