<filename>qml_models_201812170942.sql
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(1,'QXgb','{"booster": "gbtree", "eta": 0.005, "eval_metric": "auc", "max_depth": 3, "num_boost_round": 1000, "objective": "binary:logistic"}','simple xgb linear','predict',1)
,(2,'QXgb','{"booster": "gblinear", "eta": 0.1, "eval_metric": "auc", "max_depth": 3, "num_boost_round": 100, "objective": "binary:logistic", "subsample": 0.5}','simple xgb linear','predict',1)
,(1000,'QXgb','{}','dummy','predict',1)
,(156,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.6, "eta": 0.0725, "eval_metric": "auc", "gamma": 0.04, "max_depth": 1, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(157,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.5, "eta": 0.0089, "eval_metric": "auc", "gamma": 0.008, "max_depth": 4, "num_boost_round": 1550, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(158,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.7, "eta": 0.1594, "eval_metric": "auc", "gamma": 0.2, "max_depth": 3, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(159,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.9, "eta": 0.004, "eval_metric": "auc", "gamma": 0.002, "max_depth": 5, "num_boost_round": 321, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(160,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.8, "eta": 0.5917, "eval_metric": "auc", "gamma": 0.0, "max_depth": 5, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(161,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.5, "eta": 0.0024, "eval_metric": "auc", "gamma": 0.04, "max_depth": 4, "num_boost_round": 190, "objective": "binary:logistic", "subsample": 0.4, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(162,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.6, "eta": 0.0089, "eval_metric": "auc", "gamma": 0.2, "max_depth": 3, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(163,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.4, "eta": 0.0002, "eval_metric": "auc", "gamma": 1.0, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(164,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.8, "eta": 0.0003, "eval_metric": "auc", "gamma": 0.04, "max_depth": 5, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(165,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.5, "eta": 0.0002, "eval_metric": "auc", "gamma": 0.2, "max_depth": 3, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(166,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 1.0, "eta": 0.0943, "eval_metric": "auc", "gamma": 0.008, "max_depth": 4, "num_boost_round": 247, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(167,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.9, "eta": 0.0011, "eval_metric": "auc", "gamma": 0.04, "max_depth": 2, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(168,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.0068, "eval_metric": "auc", "gamma": 0.2, "max_depth": 5, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(169,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.6, "eta": 0.4552, "eval_metric": "auc", "gamma": 0.04, "max_depth": 4, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(170,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 1.0, "eta": 0.0068, "eval_metric": "auc", "gamma": 1.0, "max_depth": 4, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(171,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.7, "eta": 0.7692, "eval_metric": "auc", "gamma": 0.2, "max_depth": 2, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.4, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(172,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.8, "eta": 0.0014, "eval_metric": "auc", "gamma": 0.2, "max_depth": 2, "num_boost_round": 417, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(173,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.5, "eta": 0.0003, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 321, "objective": "binary:logistic", "subsample": 0.4, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(174,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.8, "eta": 0.0014, "eval_metric": "auc", "gamma": 0.0, "max_depth": 5, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(175,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.5, "eta": 0.0558, "eval_metric": "auc", "gamma": 0.04, "max_depth": 3, "num_boost_round": 321, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(176,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.4, "colsample_bytree": 0.6, "eta": 0.0254, "eval_metric": "auc", "gamma": 0.0, "max_depth": 1, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(177,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.7, "eta": 0.0008, "eval_metric": "auc", "gamma": 0.002, "max_depth": 3, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(178,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.9, "eta": 0.0195, "eval_metric": "auc", "gamma": 0.002, "max_depth": 5, "num_boost_round": 2619, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(179,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.4, "colsample_bytree": 0.6, "eta": 0.0006, "eval_metric": "auc", "gamma": 1.0, "max_depth": 3, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(180,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.4, "eta": 0.0031, "eval_metric": "auc", "gamma": 0.0, "max_depth": 1, "num_boost_round": 1550, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(181,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.7, "eta": 0.0254, "eval_metric": "auc", "gamma": 1.0, "max_depth": 5, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(182,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.9, "eta": 0.0005, "eval_metric": "auc", "gamma": 0.002, "max_depth": 3, "num_boost_round": 2619, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(183,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.0018, "eval_metric": "auc", "gamma": 0.008, "max_depth": 5, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(184,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.4, "colsample_bytree": 0.6, "eta": 0.015, "eval_metric": "auc", "gamma": 0.2, "max_depth": 1, "num_boost_round": 146, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(185,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.7, "eta": 0.0429, "eval_metric": "auc", "gamma": 1.0, "max_depth": 5, "num_boost_round": 1550, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(186,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.6, "eta": 0.0053, "eval_metric": "auc", "gamma": 0.0, "max_depth": 2, "num_boost_round": 417, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(187,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.7, "eta": 0.2693, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(188,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.8, "eta": 0.0116, "eval_metric": "auc", "gamma": 0.2, "max_depth": 1, "num_boost_round": 1550, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(189,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.4, "eta": 0.0031, "eval_metric": "auc", "gamma": 0.002, "max_depth": 5, "num_boost_round": 417, "objective": "binary:logistic", "subsample": 0.6, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(190,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.9, "eta": 0.0943, "eval_metric": "auc", "gamma": 0.04, "max_depth": 3, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(191,'QXgb','{"alpha": 0.0, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 1.0, "eta": 0.0089, "eval_metric": "auc", "gamma": 0.008, "max_depth": 5, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(192,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.6, "eta": 0.0005, "eval_metric": "auc", "gamma": 1.0, "max_depth": 3, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.5, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(193,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.5, "eta": 0.0018, "eval_metric": "auc", "gamma": 0.0, "max_depth": 5, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(194,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.4, "eta": 0.1594, "eval_metric": "auc", "gamma": 0.04, "max_depth": 2, "num_boost_round": 705, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(195,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.5, "eta": 0.004, "eval_metric": "auc", "gamma": 0.2, "max_depth": 4, "num_boost_round": 112, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(196,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.5, "eta": 0.033, "eval_metric": "auc", "gamma": 0.002, "max_depth": 3, "num_boost_round": 247, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(197,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.5, "eta": 0.2693, "eval_metric": "auc", "gamma": 0.002, "max_depth": 3, "num_boost_round": 190, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(198,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.4, "eta": 0.0429, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 190, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(199,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.4, "eta": 0.0943, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 112, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(200,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.4, "eta": 0.2072, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 146, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(201,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.4, "eta": 0.4552, "eval_metric": "auc", "gamma": 0.04, "max_depth": 3, "num_boost_round": 247, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(202,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.6, "eta": 0.0558, "eval_metric": "auc", "gamma": 0.04, "max_depth": 4, "num_boost_round": 190, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(203,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.5, "eta": 0.0116, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 417, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(204,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.4, "eta": 0.1226, "eval_metric": "auc", "gamma": 0.2, "max_depth": 2, "num_boost_round": 190, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(205,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.6, "eta": 0.0195, "eval_metric": "auc", "gamma": 0.04, "max_depth": 1, "num_boost_round": 146, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(206,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.7, "eta": 1.0, "eval_metric": "auc", "gamma": 0.04, "max_depth": 3, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(207,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.5, "eta": 0.0068, "eval_metric": "auc", "gamma": 0.2, "max_depth": 4, "num_boost_round": 321, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(208,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.6, "eta": 0.0429, "eval_metric": "auc", "gamma": 1.0, "max_depth": 3, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(209,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.5, "eta": 0.0725, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 247, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(210,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.8, "eta": 0.015, "eval_metric": "auc", "gamma": 0.002, "max_depth": 2, "num_boost_round": 1192, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(211,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.5, "colsample_bytree": 0.4, "eta": 0.4552, "eval_metric": "auc", "gamma": 0.2, "max_depth": 1, "num_boost_round": 321, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(212,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.4, "colsample_bytree": 0.7, "eta": 0.0053, "eval_metric": "auc", "gamma": 0.008, "max_depth": 3, "num_boost_round": 2619, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
(213,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 1.0, "eta": 0.0001, "eval_metric": "auc", "gamma": 1.0, "max_depth": 4, "num_boost_round": 2015, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(214,'QXgb','{"alpha": 0.008, "booster": "gbtree", "colsample_bylevel": 0.6, "colsample_bytree": 0.8, "eta": 0.0011, "eval_metric": "auc", "gamma": 0.2, "max_depth": 3, "num_boost_round": 1550, "objective": "binary:logistic", "subsample": 0.9, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(215,'QXgb','{"alpha": 0.2, "booster": "gbtree", "colsample_bylevel": 1.0, "colsample_bytree": 0.5, "eta": 0.0254, "eval_metric": "auc", "gamma": 0.04, "max_depth": 3, "num_boost_round": 917, "objective": "binary:logistic", "subsample": 1.0, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(216,'QXgb','{"alpha": 0.04, "booster": "gbtree", "colsample_bylevel": 0.9, "colsample_bytree": 0.6, "eta": 0.0024, "eval_metric": "auc", "gamma": 0.008, "max_depth": 2, "num_boost_round": 417, "objective": "binary:logistic", "subsample": 0.4, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(217,'QXgb','{"alpha": 0.002, "booster": "gbtree", "colsample_bylevel": 0.7, "colsample_bytree": 0.7, "eta": 0.0002, "eval_metric": "auc", "gamma": 0.04, "max_depth": 1, "num_boost_round": 542, "objective": "binary:logistic", "subsample": 0.7, "tree_method": "hist"}','hyperopt xgb','predict',1)
,(218,'QXgb','{"alpha": 1.0, "booster": "gbtree", "colsample_bylevel": 0.8, "colsample_bytree": 0.6, "eta": 0.033, "eval_metric": "auc", "gamma": 0.002, "max_depth": 3, "num_boost_round": 112, "objective": "binary:logistic", "subsample": 0.8, "tree_method": "hist"}','hyperopt xgb','predict',1)
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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;
INSERT INTO qml_models (model_id,cls,params,descr,predict_fn,"level") VALUES 
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,(665,'QAvgOneModelData','{"model_id": 636, "times": 8}','','predict',-2)
;