docs: def explain_prediction_linear_regressor(reg, doc,
                                          vec=None,
                                          top=None,
                                          top_targets=None,
                                          target_names=None,
                                          targets=None,
                                          feature_names=None,
                                          feature_re=None,
                                          feature_filter=None,
                                          vectorized=False):
      """
      Explain prediction of a linear regressor.
  
      See :func:`eli5.explain_prediction` for description of
      ``top``, ``top_targets``, ``target_names``, ``targets``,
      ``feature_names``, ``feature_re`` and ``feature_filter`` parameters.
  
      ``vec`` is a vectorizer instance used to transform
      raw features to the input of the classifier ``clf``;
      you can pass it instead of ``feature_names``.
  
      ``vectorized`` is a flag which tells eli5 if ``doc`` should be
      passed through ``vec`` or not. By default it is False, meaning that
      if ``vec`` is not None, ``vec.transform([doc])`` is passed to the
      regressor ``reg``. Set it to True if you're passing ``vec``,
      but ``doc`` is already vectorized.
      """
      if isinstance(reg, (SVR, NuSVR)) and reg.kernel != 'linear':
          return explain_prediction_sklearn_not_supported(reg, doc)
  
      vec, feature_names = handle_vec(reg, doc, vec, vectorized, feature_names)
      X = get_X(doc, vec=vec, vectorized=vectorized, to_dense=True)
  
      score, = reg.predict(X)
  
      if has_intercept(reg):
          X = add_intercept(X)
      x = get_X0(X)
  
      feature_names, flt_indices = feature_names.handle_filter(
          feature_filter, feature_re, x)
  
      res = Explanation(
          estimator=repr(reg),
          method='linear model',
          targets=[],
          is_regression=True,
      )
      assert res.targets is not None
  
      _weights = _linear_weights(reg, x, top, feature_names, flt_indices)
      names = get_default_target_names(reg)
      display_names = get_target_display_names(names, target_names, targets,
                                               top_targets, score)
  
      if is_multitarget_regressor(reg):
          for label_id, label in display_names:
              target_expl = TargetExplanation(
                  target=label,
                  feature_weights=_weights(label_id),
                  score=score[label_id],
              )
              add_weighted_spans(doc, vec, vectorized, target_expl)
              res.targets.append(target_expl)
      else:
          target_expl = TargetExplanation(
              target=display_names[0][1],
              feature_weights=_weights(0),
              score=score,
          )
          add_weighted_spans(doc, vec, vectorized, target_expl)
          res.targets.append(target_expl)
  
      return res