catboost
- catboost 超参官方文档
https://catboost.ai/docs/concepts/python-reference_parameters-list.html
- 用HyperOpt调参的博客
https://effectiveml.com/using-grid-search-to-optimise-catboost-parameters.html
"catboost": {
"max_depth": {"_type": "int_quniform", "_value": [1,15],"_default": 7},
"learning_rate": {"_type": "loguniform", "_value": [1e-3,0.2],"_default": 0.1},
"_n_estimators-lr_ratio": {"_type": "loguniform", "_value": [0.1,100],"_default": 10},
"subsample": {"_type": "quniform", "_value": [0.1,1],"_default": 1},
"l2_leaf_reg": {"_type": "loguniform","_value": [0.1,100], "_default": 3},
"border_count": {"_type": "int_loguniform","_value": [1,1000], "_default": 0},
"early_stopping_rounds": 500,
"thread_count": 1
},
https://juejin.im/post/5b76437ae51d45666b5d9b05
https://www.kaggle.com/donkeys/lgbm-with-hyperopt-tuning
"lightgbm": {
"boosting_type": {"_type": "choice", "_value":["gbdt","dart","goss"],"_default": "gbdt"},
"num_leaves": {"_type": "int_quniform", "_value": [10,150],"_default": 31},
"max_depth": {"_type": "int_quniform", "_value": [1,100],"_default": 31},
"learning_rate": {"_type": "loguniform", "_value": [1e-3,0.2],"_default": 0.1},
"subsample_for_bin": {"_type": "int_quniform","_value": [2e4, 3e5, 2e4],"_default": 2e5},
"_n_estimators-lr_ratio": {"_type": "loguniform", "_value": [0.1,100],"_default": 10},
"feature_fraction": {"_type": "quniform","_value": [0.5,1,0.05],"_default": 1},
"bagging_fraction": {"_type": "quniform","_value": [0.5,1,0.05],"_default": 1}, //alias "subsample" todo
"min_data_in_leaf": {"_type": "int_quniform","_value": [0,6,1],"_default": 1},
"lambda_l1": {"_type": "loguniform","_value": [1e-7,10], "_default": 0},
"lambda_l2": {"_type": "loguniform","_value": [1e-7,10], "_default": 0},
"min_child_weight": {"_type": "loguniform","_value": [1e-7,10], "_default": 1e-3},
"early_stopping_rounds": 500
},
