def METHOD_NAME(self, accuracy=10, **kwargs):
    if accuracy > 8:
        estimators_list = [100, 200, 300, 500, 1000, 2000]
    elif accuracy >= 5:
        estimators_list = [50, 100, 200, 300, 400, 500]
    elif accuracy >= 3:
        estimators_list = [10, 50, 100]
    elif accuracy >= 2:
        estimators_list = [10, 50]
    else:
        estimators_list = [10]
    if config.hard_asserts:
        # for testing avoid too many trees
        estimators_list = [10]
    # Modify certain parameters for tuning
    self.params["n_estimators"] = int(np.random.choice(estimators_list))
    self.params["criterion"] = np.random.choice(["gini", "entropy"]) if self.num_classes >= 2 \
        else np.random.choice(["mse", "mae"])
    max_depth_list = [6, 10, 14, 20]
    self.params['max_depth'] = int(np.random.choice(max_depth_list))
    min_samples_split_list = [2, 4, 10]
    self.params['min_samples_split'] = int(np.random.choice(min_samples_split_list))
    min_samples_leaf_list = [1, 2, 5]
    self.params['min_samples_leaf'] = int(np.random.choice(min_samples_leaf_list))
    oob_score_list = [True, False]
    self.params['oob_score'] = bool(np.random.choice(oob_score_list))