我正在使用以下设置运行带有catboost的catboost分类器:
model = CatBoostClassifier(iterations=1000, learning_rate=0.05, depth=7, loss_function='MultiClass',calc_feature_importance=True)
我有5个类,它从-ve值开始,在拟合模型时增加如下:
0: learn: -1.5036342 test: -1.5039740 best: -1.5039740 (0) total: 18s remaining: 4h 59m 46s
1: learn: -1.4185548 test: -1.4191364 best: -1.4191364 (1) total: 37.8s remaining: 5h 14m 24s
2: learn: -1.3475387 test: -1.3482641 best: -1.3482641 (2) total: 56.3s remaining: 5h 12m 1s
3: learn: -1.2868831 test: -1.2877465 best: -1.2877465 (3) total: 1m 15s remaining: 5h 12m 32s
4: learn: -1.2342138 test: -1.2351585 best: -1.2351585 (4) total: 1m 34s remaining: 5h 13m 56s
这是正常行为吗?而在大多数机器学习算法中,对数损失是正的,随训练而减小。我这里缺什么?
