code: 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
  
imports: from functools import partial
  import numpy
  import scipy.sparse
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor, GradientBoostingClassifier, GradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor
  from sklearn.linear_model import ElasticNet, ElasticNetCV, HuberRegressor, Lars, LassoCV, LinearRegression, LogisticRegression, LogisticRegressionCV, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, PassiveAggressiveClassifier, PassiveAggressiveRegressor, Perceptron, Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, SGDClassifier, SGDRegressor, TheilSenRegressor
  from sklearn.svm import LinearSVC, LinearSVR, SVC, SVR, NuSVC, NuSVR, OneClassSVM
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from eli5.base import Explanation, TargetExplanation
  from eli5.base_utils import singledispatch
  from eli5.utils import get_target_display_names, get_binary_target_scale_label_id
  from eli5.sklearn.utils import add_intercept, get_coef, get_default_target_names, get_X, get_X0, is_multiclass_classifier, is_multitarget_regressor, predict_proba, has_intercept, handle_vec
  from eli5.sklearn.text import add_weighted_spans
  from eli5.explain import explain_prediction
  from eli5._decision_path import DECISION_PATHS_CAVEATS
  from eli5._feature_weights import get_top_features_filtered
label_desc: Use image node_id 8 for calling a global function with example usage: explain_prediction_linear_regressor(reg, doc, vec, top, top_targets, target_names, targets, feature_names, feature_re, feature_filter, vectorized) and returns: res, explain_prediction_sklearn_not_supported