code: def test_get_feature_importances(boston_train):
      X, y, feat_names = boston_train
      svr = SVR(C=20, gamma="auto").fit(X, y)
      score, importances = get_score_importances(svr.score, X, y)
      assert score > 0.7
      importances = dict(zip(feat_names, np.mean(importances, axis=0)))
      print(score)
      print(importances)
      assert importances["AGE"] > importances["NOX"]
      assert importances["B"] > importances["CHAS"]
  
file_path: eli5/tests/test_permutation_importance.py
imports: from __future__ import absolute_import
  import numpy
  from sklearn.svm import SVR
  from eli5.permutation_importance import iter_shuffled, get_score_importances
label_desc: Use image node_id 4 for calling a global function with example usage: test_get_feature_importances(boston_train) without return types