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amitkumarj441/xgb_lgb_log.txt

Created May 29, 2019 19:10
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Training logs
Raw
xgb_lgb_log.txt
For LGBM on F1 set
``````````````
working fold 0
fold 0
Training until validation scores don't improve for 200 rounds.
[1000] training's l1: 2.13847 valid_1's l1: 2.31434
[2000] training's l1: 1.81812 valid_1's l1: 1.90579
[3000] training's l1: 1.60539 valid_1's l1: 1.73527
[4000] training's l1: 1.43323 valid_1's l1: 1.64906
[5000] training's l1: 1.28761 valid_1's l1: 1.58606
[6000] training's l1: 1.16084 valid_1's l1: 1.5583
Early stopping, best iteration is:
[5872] training's l1: 1.17555 valid_1's l1: 1.55345
MAE: 1.553450
RMSE: 4.025651
working fold 1
fold 1
Training until validation scores don't improve for 200 rounds.
[1000] training's l1: 2.09091 valid_1's l1: 2.79079
[2000] training's l1: 1.74237 valid_1's l1: 2.37614
[3000] training's l1: 1.50526 valid_1's l1: 2.28815
Early stopping, best iteration is:
[3599] training's l1: 1.38783 valid_1's l1: 2.26417
MAE: 2.264169
RMSE: 7.183851
working fold 2
fold 2
Training until validation scores don't improve for 200 rounds.
[1000] training's l1: 2.17702 valid_1's l1: 1.60301
[2000] training's l1: 1.82948 valid_1's l1: 1.45522
Early stopping, best iteration is:
[2049] training's l1: 1.81603 valid_1's l1: 1.4527
MAE: 1.452704
RMSE: 3.577650
working fold 3
fold 3
Training until validation scores don't improve for 200 rounds.
[1000] training's l1: 2.02332 valid_1's l1: 2.93532
[2000] training's l1: 1.69278 valid_1's l1: 2.80693
[3000] training's l1: 1.5058 valid_1's l1: 2.75415
Early stopping, best iteration is:
[3565] training's l1: 1.40815 valid_1's l1: 2.72774
MAE: 2.727737
RMSE: 13.411262
working fold 4
fold 4
Training until validation scores don't improve for 200 rounds.
[1000] training's l1: 2.13121 valid_1's l1: 2.14748
[2000] training's l1: 1.79306 valid_1's l1: 1.93374
[3000] training's l1: 1.56535 valid_1's l1: 1.85636
[4000] training's l1: 1.39135 valid_1's l1: 1.81068
Early stopping, best iteration is:
[3954] training's l1: 1.39798 valid_1's l1: 1.8087
MAE: 1.808703
RMSE: 6.435700
working fold 5
fold 5
Training until validation scores don't improve for 200 rounds.
Early stopping, best iteration is:
[203] training's l1: 2.57899 valid_1's l1: 3.36429
MAE: 3.364293
RMSE: 18.518604
working fold 6
fold 6
Training until validation scores don't improve for 200 rounds.
Early stopping, best iteration is:
[2] training's l1: 2.96829 valid_1's l1: 2.76239
MAE: 2.762392
RMSE: 9.058714
working fold 7
fold 7
Training until validation scores don't improve for 200 rounds.
Early stopping, best iteration is:
[289] training's l1: 2.66274 valid_1's l1: 1.9085
MAE: 1.908500
RMSE: 6.037961
MAEs [1.5534497875807807, 2.264169025958974, 1.4527037510434297, 2.7277368763531564, 1.8087031334605395, 3.364292891238353, 2.762391618778329, 1.9085000961743128]
MAE mean: 2.230243
RMSEs [4.025651255389721, 7.183850870205878, 3.5776501127673805, 13.411261738706003, 6.435700104558473, 18.518603660181604, 9.058714191044146, 6.037961242393822]
RMSE mean: 8.531174
```````````````````
For XGB on F1 set:
``````````````````````````````````````````
Fold 0 started at Wed May 29 18:42:39 2019
[0] train-mae:5.65218 valid_data-mae:5.76433
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
[799] train-mae:0.098571 valid_data-mae:2.08026
Fold 1 started at Wed May 29 18:42:50 2019
[0] train-mae:5.48583 valid_data-mae:6.8176
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
Stopping. Best iteration:
[547] train-mae:0.465611 valid_data-mae:2.36352
Fold 2 started at Wed May 29 18:43:00 2019
[0] train-mae:5.78591 valid_data-mae:4.80901
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
Stopping. Best iteration:
[427] train-mae:1.219 valid_data-mae:2.14559
Fold 3 started at Wed May 29 18:43:09 2019
[0] train-mae:5.56306 valid_data-mae:6.42456
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
Stopping. Best iteration:
[462] train-mae:0.885326 valid_data-mae:2.5545
Fold 4 started at Wed May 29 18:43:18 2019
[0] train-mae:5.81558 valid_data-mae:4.59393
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
[799] train-mae:0.09816 valid_data-mae:1.89183
Fold 5 started at Wed May 29 18:43:29 2019
[0] train-mae:5.63753 valid_data-mae:5.88442
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
[799] train-mae:0.092851 valid_data-mae:2.64772
Fold 6 started at Wed May 29 18:43:40 2019
[0] train-mae:5.67518 valid_data-mae:5.6117
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
Stopping. Best iteration:
[422] train-mae:1.21521 valid_data-mae:2.68367
Fold 7 started at Wed May 29 18:43:49 2019
[0] train-mae:5.72122 valid_data-mae:5.27803
Multiple eval metrics have been passed: 'valid_data-mae' will be used for early stopping.
Will train until valid_data-mae hasn't improved in 200 rounds.
Stopping. Best iteration:
[526] train-mae:0.58677 valid_data-mae:2.27403
CV mean score: 2.326391.
`````````````````````````
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