LightGBM 获取模型决策规则-> 模型解释

如何解决LightGBM 获取模型决策规则-> 模型解释

我需要解释二元分类的模型决策。这是我的模型：

lgbm_params = {
    'application': 'binary','boosting': 'gbdt'
    
}

# Fit the model
evaluation_results={}
clf = lgb.train(train_set=df_train,params=lgbm_params,valid_sets=[df_train,df_val],valid_names=['Train','Test'],evals_result=evaluation_results
                 num_boost_round=500,early_stopping_rounds=200,verbose_eval=20
                )

我从 clf.dump_model()['tree_info'] 中解析了所有的 Leaf_values

tree_index: 0
{'leaf_index': 0,'leaf_value': -3.040727504018978,'leaf_weight': 47528.00472457707,'leaf_count': 1004195}
{'leaf_index': 13,'leaf_value': -2.9202709057364595,'leaf_weight': 1323.3580410331488,'leaf_count': 26449}
{'leaf_index': 14,'leaf_value': -2.9427631652658075,'leaf_weight': 109.50735418498516,'leaf_count': 2212}
{'leaf_index': 17,'leaf_value': -1.4929745840872577,'leaf_weight': 302.07093365490437,'leaf_count': 1949}
{'leaf_index': 16,'leaf_value': -2.9837542927288503,'leaf_weight': 210.75128224492073,'leaf_count': 4339}
{'leaf_index': 11,'leaf_value': -3.066253913323111,'leaf_weight': 48.197187930345535,'leaf_count': 1030}
{'leaf_index': 19,'leaf_value': -1.0782095500999522,'leaf_weight': 121.46848337352276,'leaf_count': 306}
{'leaf_index': 4,'leaf_value': -1.0109852904649248,'leaf_weight': 1215.3730453550816,'leaf_count': 2287}
{'leaf_index': 10,'leaf_value': -2.5625965199023018,'leaf_weight': 176.44212743639946,'leaf_count': 2928}
{'leaf_index': 5,'leaf_value': -2.795752338152544,'leaf_weight': 2099.699764251709,'leaf_count': 39486}
{'leaf_index': 2,'leaf_value': -0.9452821425348215,'leaf_weight': 3836.4817948788404,'leaf_count': 4829}
{'leaf_index': 15,'leaf_value': -2.5710742313240202,'leaf_weight': 70.22461323440075,'leaf_count': 1171}
{'leaf_index': 7,'leaf_value': -2.003150600405347,'leaf_weight': 514.2693380862474,'leaf_count': 5806}
{'leaf_index': 18,'leaf_value': -3.01381609888255,'leaf_weight': 221.04787051677704,'leaf_count': 4614}
{'leaf_index': 1,'leaf_value': -3.0336576123606727,'leaf_weight': 2667.005198687315,'leaf_count': 56171}
{'leaf_index': 6,'leaf_value': -1.0930359013819269,'leaf_weight': 630.5078001469374,'leaf_count': 1677}
{'leaf_index': 3,'leaf_value': -2.6955877051335113,'leaf_weight': 83.39051477611065,'leaf_count': 1489}
{'leaf_index': 12,'leaf_value': -0.9320870000416277,'leaf_weight': 47102.81295746565,'leaf_count': 53395}
{'leaf_index': 9,'leaf_value': -2.339730727422376,'leaf_weight': 188.79415191709995,'leaf_count': 2734}
{'leaf_index': 8,'leaf_value': -2.903086908469149,'leaf_weight': 201.27797167003155,'leaf_count': 3990}

tree_index: 1
{'leaf_index': 0,'leaf_value': 1.7003719046589805,'leaf_weight': 89.89699831604958,'leaf_count': 526}
{'leaf_index': 6,'leaf_value': -0.008478093867401493,'leaf_weight': 606.4826318323612,'leaf_count': 12085}
{'leaf_index': 18,'leaf_value': 0.35854053128585933,'leaf_weight': 21147.70379064977,'leaf_count': 7521}
{'leaf_index': 16,'leaf_value': 1.7996142884045683,'leaf_weight': 101.33498433232307,'leaf_count': 316}
{'leaf_index': 19,'leaf_value': 0.6181301642947885,'leaf_weight': 85.0488400682807,'leaf_count': 1175}
{'leaf_index': 17,'leaf_value': 0.2108335293509681,'leaf_weight': 161.64981354773045,'leaf_count': 331}
{'leaf_index': 15,'leaf_value': -0.016521274681180857,'leaf_weight': 423.0099447518587,'leaf_count': 7536}
{'leaf_index': 4,'leaf_value': -0.06392728087564778,'leaf_weight': 1595.620696157217,'leaf_count': 32539}
{'leaf_index': 2,'leaf_value': -0.08846515759106144,'leaf_weight': 41799.491890221834,'leaf_count': 953595}
{'leaf_index': 12,'leaf_value': 0.3285022589016781,'leaf_weight': 520.416460648179,'leaf_count': 1191}
{'leaf_index': 9,'leaf_value': -0.013072092095162056,'leaf_weight': 1560.870567932725,'leaf_count': 26635}
{'leaf_index': 14,'leaf_value': 0.29746060409739705,'leaf_weight': 973.760700315237,'leaf_count': 12824}
{'leaf_index': 11,'leaf_value': 1.2303136226518105,'leaf_weight': 78.05999701470137,'leaf_count': 449}
{'leaf_index': 8,'leaf_value': 1.5758433007286676,'leaf_weight': 44.83048837631941,'leaf_count': 315}
{'leaf_index': 7,'leaf_value': -0.10539180772863799,'leaf_weight': 805.8025468811393,'leaf_count': 16600}
{'leaf_index': 13,'leaf_value': 0.26696944007068507,'leaf_weight': 1808.0697652101517,'leaf_count': 17543}
{'leaf_index': 5,'leaf_value': 0.29779065829637386,'leaf_weight': 1361.8919109031558,'leaf_count': 10420}
{'leaf_index': 1,'leaf_value': -0.017633350811942006,'leaf_weight': 852.0203822031617,'leaf_count': 17298}
{'leaf_index': 10,'leaf_value': 0.3528788566582046,'leaf_weight': 207151.9944522865,'leaf_count': 64972}
{'leaf_index': 3,'leaf_value': -0.07833613971012304,'leaf_weight': 1702.7047363184392,'leaf_count': 37186}

如何将这些值解释为属于 0 类或 1 类？或者我怎样才能获得决策路径？ Like -> if Feature1