class_docstr: Test class for regression tests.
code: def check_dummy_regressor_predict(
      self, model_args, train_labels, expected_output
  ):
      # create hard-coded featuresets with the given labels
      train_fs = FeatureSet(
          "regression_train",
          [f"TrainExample{i}" for i in range(20)],
          labels=train_labels,
          features=[{"feature": i} for i in range(20)],
      )
  
      test_fs = FeatureSet(
          "regression_test",
          [f"TestExample{i}" for i in range(10)],
          features=[{"feature": i} for i in range(20, 30)],
      )
  
      # Ensure predictions are as expectedfor the given strategy
      learner = Learner("DummyRegressor", model_kwargs=model_args)
      learner.train(train_fs, grid_search=False)
      predictions = learner.predict(test_fs)
      self.assertEqual(
          np.array_equal(expected_output, predictions), True
      )
  
imports: import math
  import re
  import unittest
  import warnings
  from itertools import chain, product
  import numpy
  from numpy.testing import assert_allclose, assert_array_equal
  from scipy.stats import pearsonr
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from skll.config import _setup_config_parser
  from skll.data import FeatureSet, NDJReader, NDJWriter
  from skll.experiments import run_configuration
  from skll.learner import Learner
  from skll.learner.utils import rescaled
  from skll.utils.constants import CLASSIFICATION_ONLY_METRICS
  from skll.utils.testing import config_dir, fill_in_config_paths_for_fancy_output, make_regression_data, other_dir, output_dir, test_dir, train_dir, unlink
label_desc: Use image node_id 26 for calling the TestRegression obj's underlying member method code with example usage: obj.check_dummy_regressor_predict(model_args, train_labels, expected_output) without return types