def METHOD_NAME(cls):
    cls.X, cls.y = datasets.make_classification(
        n_samples=100,
        n_features=5,
        n_informative=4,
        n_redundant=1,
        n_classes=2,
        n_clusters_per_class=3,
        n_repeated=0,
        shuffle=False,
        random_state=0,
    )
    cls.params = {
        "metric": "binary_logloss",
        "num_leaves": "2",
        "learning_rate": 0.1,
        "feature_fraction": 0.8,
        "bagging_fraction": 0.8,
        "bagging_freq": 1,
        "seed": 1,
        "early_stopping_rounds": 0,
    }