Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.789392	training's binary_logloss: 0.578752	valid_1's auc: 0.773208	valid_1's binary_logloss: 0.584832
[400]	training's auc: 0.800083	training's binary_logloss: 0.547232	valid_1's auc: 0.773773	valid_1's binary_logloss: 0.56219
[600]	training's auc: 0.810649	training's binary_logloss: 0.532177	valid_1's auc: 0.774668	valid_1's binary_logloss: 0.556256
[800]	training's auc: 0.821325	training's binary_logloss: 0.521558	valid_1's auc: 0.775217	valid_1's binary_logloss: 0.554556
[1000]	training's auc: 0.831154	training's binary_logloss: 0.512736	valid_1's auc: 0.775269	valid_1's binary_logloss: 0.554112
Early stopping, best iteration is:
[870]	training's auc: 0.825007	training's binary_logloss: 0.518329	valid_1's auc: 0.775402	valid_1's binary_logloss: 0.554351
Fold  1 AUC : 0.775402
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.791407	training's binary_logloss: 0.577465	valid_1's auc: 0.75649	valid_1's binary_logloss: 0.592267
[400]	training's auc: 0.802093	training's binary_logloss: 0.545747	valid_1's auc: 0.75837	valid_1's binary_logloss: 0.57137
[600]	training's auc: 0.812232	training's binary_logloss: 0.530778	valid_1's auc: 0.759681	valid_1's binary_logloss: 0.565757
[800]	training's auc: 0.822218	training's binary_logloss: 0.520356	valid_1's auc: 0.760617	valid_1's binary_logloss: 0.56406
[1000]	training's auc: 0.831826	training's binary_logloss: 0.511492	valid_1's auc: 0.761223	valid_1's binary_logloss: 0.563485
[1200]	training's auc: 0.84137	training's binary_logloss: 0.503284	valid_1's auc: 0.761405	valid_1's binary_logloss: 0.563413
[1400]	training's auc: 0.85006	training's binary_logloss: 0.495879	valid_1's auc: 0.761411	valid_1's binary_logloss: 0.563482
Early stopping, best iteration is:
[1235]	training's auc: 0.842915	training's binary_logloss: 0.50194	valid_1's auc: 0.761504	valid_1's binary_logloss: 0.563355
Fold  2 AUC : 0.761504
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.790601	training's binary_logloss: 0.577889	valid_1's auc: 0.759194	valid_1's binary_logloss: 0.590355
[400]	training's auc: 0.801342	training's binary_logloss: 0.546124	valid_1's auc: 0.761742	valid_1's binary_logloss: 0.567863
[600]	training's auc: 0.811515	training's binary_logloss: 0.531096	valid_1's auc: 0.762837	valid_1's binary_logloss: 0.561915
[800]	training's auc: 0.821298	training's binary_logloss: 0.520804	valid_1's auc: 0.763339	valid_1's binary_logloss: 0.560295
[1000]	training's auc: 0.831061	training's binary_logloss: 0.512052	valid_1's auc: 0.763698	valid_1's binary_logloss: 0.559725
[1200]	training's auc: 0.840207	training's binary_logloss: 0.504104	valid_1's auc: 0.763647	valid_1's binary_logloss: 0.559686
Early stopping, best iteration is:
[1033]	training's auc: 0.832642	training's binary_logloss: 0.510674	valid_1's auc: 0.763779	valid_1's binary_logloss: 0.559648
Fold  3 AUC : 0.763779
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.790684	training's binary_logloss: 0.577864	valid_1's auc: 0.760275	valid_1's binary_logloss: 0.58962
[400]	training's auc: 0.801148	training's binary_logloss: 0.546246	valid_1's auc: 0.762826	valid_1's binary_logloss: 0.567714
[600]	training's auc: 0.811247	training's binary_logloss: 0.53133	valid_1's auc: 0.764295	valid_1's binary_logloss: 0.561506
[800]	training's auc: 0.821473	training's binary_logloss: 0.520861	valid_1's auc: 0.764594	valid_1's binary_logloss: 0.559854
[1000]	training's auc: 0.831251	training's binary_logloss: 0.512031	valid_1's auc: 0.764988	valid_1's binary_logloss: 0.559128
[1200]	training's auc: 0.84027	training's binary_logloss: 0.504285	valid_1's auc: 0.765109	valid_1's binary_logloss: 0.558935
[1400]	training's auc: 0.848786	training's binary_logloss: 0.497049	valid_1's auc: 0.765401	valid_1's binary_logloss: 0.558775
[1600]	training's auc: 0.856597	training's binary_logloss: 0.490171	valid_1's auc: 0.765302	valid_1's binary_logloss: 0.558812
Early stopping, best iteration is:
[1529]	training's auc: 0.853847	training's binary_logloss: 0.492594	valid_1's auc: 0.765417	valid_1's binary_logloss: 0.558726
Fold  4 AUC : 0.765417
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.790286	training's binary_logloss: 0.578626	valid_1's auc: 0.768811	valid_1's binary_logloss: 0.58405
[400]	training's auc: 0.800174	training's binary_logloss: 0.547254	valid_1's auc: 0.769129	valid_1's binary_logloss: 0.560844
[600]	training's auc: 0.810632	training's binary_logloss: 0.532253	valid_1's auc: 0.770016	valid_1's binary_logloss: 0.554113
[800]	training's auc: 0.820693	training's binary_logloss: 0.521796	valid_1's auc: 0.770703	valid_1's binary_logloss: 0.551766
[1000]	training's auc: 0.830826	training's binary_logloss: 0.512781	valid_1's auc: 0.771093	valid_1's binary_logloss: 0.55095
[1200]	training's auc: 0.83967	training's binary_logloss: 0.504991	valid_1's auc: 0.770801	valid_1's binary_logloss: 0.550959
Early stopping, best iteration is:
[1029]	training's auc: 0.832206	training's binary_logloss: 0.511554	valid_1's auc: 0.77125	valid_1's binary_logloss: 0.550844
Fold  5 AUC : 0.771250
Full AUC score 0.770161

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.789698	training's binary_logloss: 0.578713	valid_1's auc: 0.773593	valid_1's binary_logloss: 0.584739
[400]	training's auc: 0.800657	training's binary_logloss: 0.547076	valid_1's auc: 0.774148	valid_1's binary_logloss: 0.562079
[600]	training's auc: 0.8117	training's binary_logloss: 0.531765	valid_1's auc: 0.775241	valid_1's binary_logloss: 0.555964
[800]	training's auc: 0.822934	training's binary_logloss: 0.520971	valid_1's auc: 0.775604	valid_1's binary_logloss: 0.554454
[1000]	training's auc: 0.833159	training's binary_logloss: 0.511646	valid_1's auc: 0.775816	valid_1's binary_logloss: 0.553796
[1200]	training's auc: 0.843186	training's binary_logloss: 0.503133	valid_1's auc: 0.775492	valid_1's binary_logloss: 0.553694
Early stopping, best iteration is:
[1000]	training's auc: 0.833159	training's binary_logloss: 0.511646	valid_1's auc: 0.775816	valid_1's binary_logloss: 0.553796
Fold  1 AUC : 0.775816
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.791629	training's binary_logloss: 0.577406	valid_1's auc: 0.756418	valid_1's binary_logloss: 0.592248
[400]	training's auc: 0.802457	training's binary_logloss: 0.545641	valid_1's auc: 0.758321	valid_1's binary_logloss: 0.571322
[600]	training's auc: 0.812912	training's binary_logloss: 0.530542	valid_1's auc: 0.759668	valid_1's binary_logloss: 0.565656
[800]	training's auc: 0.823868	training's binary_logloss: 0.519785	valid_1's auc: 0.760657	valid_1's binary_logloss: 0.563887
[1000]	training's auc: 0.833776	training's binary_logloss: 0.510649	valid_1's auc: 0.761322	valid_1's binary_logloss: 0.563189
[1200]	training's auc: 0.843575	training's binary_logloss: 0.502119	valid_1's auc: 0.761737	valid_1's binary_logloss: 0.562861
Early stopping, best iteration is:
[1197]	training's auc: 0.843438	training's binary_logloss: 0.50224	valid_1's auc: 0.761741	valid_1's binary_logloss: 0.562857
Fold  2 AUC : 0.761741
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.790821	training's binary_logloss: 0.577836	valid_1's auc: 0.75931	valid_1's binary_logloss: 0.590317
[400]	training's auc: 0.801874	training's binary_logloss: 0.546006	valid_1's auc: 0.76195	valid_1's binary_logloss: 0.567807
[600]	training's auc: 0.812314	training's binary_logloss: 0.530831	valid_1's auc: 0.762897	valid_1's binary_logloss: 0.561974
[800]	training's auc: 0.823388	training's binary_logloss: 0.520028	valid_1's auc: 0.763576	valid_1's binary_logloss: 0.560383
[1000]	training's auc: 0.833421	training's binary_logloss: 0.510932	valid_1's auc: 0.764037	valid_1's binary_logloss: 0.559766
[1200]	training's auc: 0.84327	training's binary_logloss: 0.502412	valid_1's auc: 0.763988	valid_1's binary_logloss: 0.559696
Early stopping, best iteration is:
[1017]	training's auc: 0.834267	training's binary_logloss: 0.510166	valid_1's auc: 0.764153	valid_1's binary_logloss: 0.55969
Fold  3 AUC : 0.764153
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.79091	training's binary_logloss: 0.577827	valid_1's auc: 0.760442	valid_1's binary_logloss: 0.589594
[400]	training's auc: 0.801624	training's binary_logloss: 0.54613	valid_1's auc: 0.762994	valid_1's binary_logloss: 0.567674
[600]	training's auc: 0.812017	training's binary_logloss: 0.531113	valid_1's auc: 0.764471	valid_1's binary_logloss: 0.56155
[800]	training's auc: 0.822886	training's binary_logloss: 0.520403	valid_1's auc: 0.764573	valid_1's binary_logloss: 0.560084
[1000]	training's auc: 0.833003	training's binary_logloss: 0.511128	valid_1's auc: 0.765087	valid_1's binary_logloss: 0.559365
[1200]	training's auc: 0.842694	training's binary_logloss: 0.502851	valid_1's auc: 0.765069	valid_1's binary_logloss: 0.559247
Early stopping, best iteration is:
[1080]	training's auc: 0.836971	training's binary_logloss: 0.507758	valid_1's auc: 0.765208	valid_1's binary_logloss: 0.559254
Fold  4 AUC : 0.765208
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.790714	training's binary_logloss: 0.578604	valid_1's auc: 0.768595	valid_1's binary_logloss: 0.584183
Early stopping, best iteration is:
[28]	training's auc: 0.781558	training's binary_logloss: 0.661882	valid_1's auc: 0.768728	valid_1's binary_logloss: 0.660462
Fold  5 AUC : 0.768728
Full AUC score 0.762044

안녕하세요. 대회 참여 중 궁금한 부분이 생겨 많은 분들과 의견 나누고 싶어 글을 남기게 되었습니다.

간단한 전처리 이후 train데이터를 validation으로 나누어 lgbm으로 돌리다가 의문이 생겼는데요.

단순 호기심에 LGBMClassifier의 max_depth 파라미터를 10에서 -1로 바꾸고 AUC Score를 비교해봤는데

각각 fold에서의 AUC Score는 크게 다르지 않았지만 Full Auc Score가 굉장히 크게 낮아졌습니다.

이렇게 달라질수 있는 원인이 있을까요???

절취선을 기준으로 위쪽이 max_depth를 10, 아래쪽이 max_depth를 -1로 지정했을 때 입니다.

이해를 돕기 위해 아래에 작성 코드를 남깁니다.(clf의 파라미터는 간소화를 위해 제거하였습니다.)

토론 게시판에서는 코드 공유 게시판과 다르게 작성 코드를 보기좋게 올릴 수 없는것 같은데 방법을 아시는 분은

댓글남겨주시면 감사하겠습니다

작성 코드:

folds = KFold(n_splits=5, shuffle=True, random_state=RANDOM)

feats = [f for f in train.columns if f not in ['voted']]

oof_preds = np.zeros(train.shape[0])

sub_preds = np.zeros(test.shape[0])

for n_fold, (train_idx, valid_idx) in enumerate(folds.split(train[feats], train['voted'])):

  train_x, train_y = train[feats].iloc[train_idx], train['voted'].iloc[train_idx]

  valid_x, valid_y = train[feats].iloc[valid_idx], train['voted'].iloc[valid_idx]

  clf = LGBMClassifier()

  clf.fit(train_x, train_y, eval_set = [(train_x, train_y), (valid_x, valid_y)],

    eval_metric='auc', verbose=200, early_stopping_rounds=200)

  oof_preds[valid_idx] = clf.predict_proba(valid_x, num_iteration=clf.best_iteration_)[:, 1]

  sub_preds += clf.predict_proba(test, num_iteration=clf.best_iteration_)[:, 1] / folds.n_splits

  print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(valid_y, oof_preds[valid_idx])))

print('Full AUC score %.6f' % roc_auc_score(train['voted'], oof_preds))