def METHOD_NAME():
    print('Classification')
    y = np.random.randint(0, kClasses, size=kRows)
    data = xgboost.DMatrix(X, label=y, weight=w)
    booster = xgboost.train({'num_class': kClasses,
                             'tree_method': 'hist',
                             'num_parallel_tree': kForests,
                             'max_depth': kMaxDepth},
                            num_boost_round=kRounds, dtrain=data)
    booster.save_model(booster_bin('cls'))
    booster.save_model(booster_json('cls'))
    cls = xgboost.XGBClassifier(tree_method='hist',
                                num_parallel_tree=kForests,
                                max_depth=kMaxDepth,
                                n_estimators=kRounds)
    cls.fit(X, y, w)
    cls.save_model(skl_bin('cls'))
    cls.save_model(skl_json('cls'))