def METHOD_NAME(
        self,
        accuracy=10,
        **kwargs
):
    if accuracy > 8:
        estimators_list = [300, 500, 1000, 2000, ]
        depth_list = [10, 20, 30, 50, 100, ]
        samples_leaf_list = [10, 20, 30, ]
    elif accuracy >= 5:
        estimators_list = [50, 100, 200, 300, ]
        depth_list = [5, 10, 15, 25, 50, ]
        samples_leaf_list = [20, 40, 60, ]
    else:
        estimators_list = [10, 20, 40, 60, ]
        depth_list = [1, 2, 3, 5, 10, ]
        samples_leaf_list = [30, 60, 90, ]
    criterion_list = ["mse", "mae", ]
    # modify certain parameters for tuning
    self.params["n_estimators"] = int(np.random.choice(estimators_list))
    self.params["criterion"] = np.random.choice(criterion_list)
    self.params["max_depth"] = int(np.random.choice(depth_list))
    self.params["min_samples_leaf"] = int(np.random.choice(samples_leaf_list))