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.
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docstr: Predicts the labels for the given dataset.
  
  Parameters
  ----------
  data_set : SklearnDataset
      A dataset for which the labels are to be predicted.
  return_proba : bool, default=False
      If `True`, also returns a probability-like class distribution.
  
  Returns
  -------
  predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
      List of predictions if the classifier was fitted on multi-label data,
      otherwise a sparse matrix of predictions.
  probas : np.ndarray[np.float32]
      List of probabilities (or confidence estimates) if `return_proba` is True.
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code: def predict(self, data_set, return_proba=False):
      """
      Predicts the labels for the given dataset.
  
      Parameters
      ----------
      data_set : SklearnDataset
          A dataset for which the labels are to be predicted.
      return_proba : bool, default=False
          If `True`, also returns a probability-like class distribution.
  
      Returns
      -------
      predictions : np.ndarray[np.int32] or csr_matrix[np.int32]
          List of predictions if the classifier was fitted on multi-label data,
          otherwise a sparse matrix of predictions.
      probas : np.ndarray[np.float32]
          List of probabilities (or confidence estimates) if `return_proba` is True.
      """
      if len(data_set) == 0:
          return empty_result(
              self.multi_label,
              self.num_classes,
              return_prediction=True,
              return_proba=return_proba,
          )
  
      proba = self.model.predict_proba(data_set.x)
  
      return prediction_result(
          proba,
          self.multi_label,
          self.num_classes,
          enc=None,
          return_proba=return_proba,
      )
  
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 3 for calling the SklearnClassifier obj's underlying member method code with example usage: obj.predict(data_set, return_proba) and returns: prediction_result, empty_result