我正在使用一个非常简单的LightGBM模型，其超参数如下。我正在尝试建立一个分类器，但不确定模型的输出是否正确。

params = {
     'boosting_type': 'gbdt','objective': 'binary','metric': {'l2','l1'},'num_leaves': 31,'learning_rate': 0.05,'feature_fraction': 0.9,'bagging_fraction': 0.8,'bagging_freq': 5,'verbose': 0
}
gbm = lgb.train(params,lgb_train,num_boost_round=100,valid_sets=lgb_eval,early_stopping_rounds=5)

该模型似乎可以构建，但是我得到的输出不是概率。 输出是这样的：

0.00221713,0.00444936,0.00088252,0.00287112,0.0199352,0.01466104,0.000574,0.00352522,0.00374068,0.00110412

现在，此处的最大值为0.07459。如何从该模型输出实际概率。