def METHOD_NAME(self,
                  accuracy=10,
                  **kwargs):
    n_jobs = -1
    list_of_nus = [0.4, 0.5, 0.6]
    list_of_kernels = ['linear', 'rbf']
    list_of_degrees = [3, 4]
    if accuracy > 8:
        list_of_nus = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
        list_of_kernels = ['linear', 'rbf', 'poly', 'sigmoid']
        list_of_degrees = [3, 4, 5, 6]
    elif accuracy >= 5:
        list_of_nus = [0.2, 0.4, 0.5, 0.6, 0.8]
    nu_index = np.random.randint(0, high=len(list_of_nus))
    kernel_index = np.random.randint(0, high=len(list_of_kernels))
    degree_index = np.random.randint(0, high=len(list_of_degrees))
    nu = list_of_nus[nu_index]
    kernel = list_of_kernels[kernel_index]
    degree = list_of_degrees[degree_index]
    self.params = {'nu': nu,
                   'kernel': kernel,
                   'degree': degree,
                   'probability': True
                   }