class_docstr: An adapter for using scikit-learn estimators.
  
  Notes
  -----
  The multi-label settings currently assumes that the underlying classifer returns a sparse
  matrix if trained on sparse data.
class_docstr_tok: ["An","adapter","for","using","scikit-learn","estimators",".","Notes","--","--","-","The","multi-label","settings","currently","assumes","that","the","underlying","classifer","returns","a","sparse","matrix","if","trained","on","sparse","data","."]
docstr: Predicts the label distribution for each sample in the given dataset.
  
  Parameters
  ----------
  data_set : SklearnDataset
      A dataset for which the labels are to be predicted.
docstr_tok: ["Predicts","the","label","distribution","for","each","sample","in","the","given","dataset",".","Parameters","--","--","--","--","--","data_set",":","SklearnDataset","A","dataset","for","which","the","labels","are","to","be","predicted","."]
code: def predict_proba(self, data_set):
      """Predicts the label distribution for each sample in the given dataset.
  
      Parameters
      ----------
      data_set : SklearnDataset
          A dataset for which the labels are to be predicted.
      """
      if len(data_set) == 0:
          return empty_result(
              self.multi_label,
              self.num_classes,
              return_prediction=False,
              return_proba=True,
          )
  
      return self.model.predict_proba(data_set.x)
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.preprocessing import normalize
  from sklearn.svm import LinearSVC
  from sklearn.utils.multiclass import is_multilabel
  from small_text.utils.classification import empty_result, prediction_result
  from small_text.utils.data import check_training_data
label_desc: Use image node_id 4 for calling the SklearnClassifier obj's underlying member method code with example usage: obj.predict_proba(data_set) and returns: self, empty_result