class_docstr: Test class for regression tests.
code: def check_adaboost_regression(self, base_estimator):
      train_fs, test_fs, _ = make_regression_data(
          num_examples=2000, sd_noise=4, num_features=3
      )
  
      # train an AdaBoostRegressor on the training data and evalute on the
      # testing data
      learner = Learner(
          "AdaBoostRegressor",
          model_kwargs={"estimator": base_estimator},
      )
      learner.train(train_fs, grid_search=False)
  
      # now generate the predictions on the test set
      predictions = learner.predict(test_fs)
  
      # now make sure that the predictions are close to
      # the actual test FeatureSet labels that we generated
      # using make_regression_data. To do this, we just
      # make sure that they are correlated
      cor, _ = pearsonr(predictions, test_fs.labels)
      self.assertGreater(cor, 0.95)
  
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 17 for calling the TestRegression obj's underlying member method code with example usage: obj.check_adaboost_regression(base_estimator) without return types