def METHOD_NAME(self) -> None:
    self.seed = 1000
    test_fraction = 0.2
    training_fraction = 0.2
    self.update_fraction = 0.1
    self.verbose = False
    self.dataset_name = 'house'
    self.num_holdout_rows = 100
    self.run = FakeRun(
        config={'threads': 8, 'analysis': {
                'parameters': {'seed': self.seed,
                               'num_holdout_rows': self.num_holdout_rows,
                               'data_summarization_fraction': 1.0}}},
        run_logger=logger)
    dataset = read_dataset(self.dataset_name)
    self.train_dataset, self.test_dataset = train_test_split(
        dataset, test_size=test_fraction,  random_state=self.seed)
    self.update_num_samples = int(
        self.train_dataset.shape[0] * self.update_fraction)
    baseline_dataset = self.train_dataset.sample(
        frac=training_fraction, random_state=self.seed)
    self.baseline_model_name = 'test_multistep_learning_baseline'
    if job_exists(self.baseline_model_name, remote=False):
        path = download_job(self.baseline_model_name)
        self.baseline_model = Job.from_file(path)
    else:
        self.baseline_model = train(self.dataset_name, baseline_dataset,
                                    verbose=self.verbose, run=self.run)
        self.baseline_model.wait_to_complete()
        path = jobs_dir / self.baseline_model_name
        self.baseline_model.store(path)
    self.used_dataset = baseline_dataset.copy()