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- XGBoost 버전 확인 ★
import xgboost
print(xgboost.__version__)
# 1.3.3
파이썬 래퍼 XGBoost 적용 – 위스콘신 Breast Cancer 데이터 셋¶
** 데이터 세트 로딩 **
import xgboost as xgb
from xgboost import plot_importance
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
dataset = load_breast_cancer()
X_features= dataset.data
y_label = dataset.target
cancer_df = pd.DataFrame(data=X_features, columns=dataset.feature_names)
cancer_df['target']= y_label
cancer_df.head(3)
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension target
0 17.99 10.38 122.8 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 ... 17.33 184.6 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890 0
1 20.57 17.77 132.9 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 ... 23.41 158.8 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902 0
2 19.69 21.25 130.0 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 ... 25.53 152.5 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758 0
3 rows × 31 columns
print(dataset.target_names)
print(cancer_df['target'].value_counts())
# ['malignant' 'benign']
# 1 357
# 0 212
# Name: target, dtype: int64
# 전체 데이터 중 80%는 학습용 데이터, 20%는 테스트용 데이터 추출
X_train, X_test, y_train, y_test=train_test_split(X_features, y_label,
test_size=0.2, random_state=156 )
print(X_train.shape , X_test.shape)
# (455, 30) (114, 30)
학습과 예측 데이터 세트를 DMatrix로 변환
dtrain = xgb.DMatrix(data=X_train , label=y_train)
dtest = xgb.DMatrix(data=X_test , label=y_test)
하이퍼 파라미터 설정
params = { 'max_depth':3,
'eta': 0.1,
'objective':'binary:logistic',
'eval_metric':'logloss',
'early_stoppings':100
}
num_rounds = 400
주어진 하이퍼 파라미터와 early stopping 파라미터를 train( ) 함수의 파라미터로 전달하고 학습
# train 데이터 셋은 ‘train’ , evaluation(test) 데이터 셋은 ‘eval’ 로 명기합니다.
wlist = [(dtrain,'train'),(dtest,'eval') ]
# 하이퍼 파라미터와 early stopping 파라미터를 train( ) 함수의 파라미터로 전달
xgb_model = xgb.train(params = params , dtrain=dtrain , num_boost_round=num_rounds , evals=wlist )
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[294] train-logloss:0.00557 eval-logloss:0.086196
[295] train-logloss:0.005563 eval-logloss:0.086221
[296] train-logloss:0.005556 eval-logloss:0.086308
[297] train-logloss:0.005548 eval-logloss:0.086178
[298] train-logloss:0.005541 eval-logloss:0.086263
[299] train-logloss:0.005534 eval-logloss:0.086131
[300] train-logloss:0.005526 eval-logloss:0.086179
[301] train-logloss:0.005519 eval-logloss:0.086052
[302] train-logloss:0.005512 eval-logloss:0.086016
[303] train-logloss:0.005505 eval-logloss:0.086101
[304] train-logloss:0.005498 eval-logloss:0.085977
[305] train-logloss:0.005491 eval-logloss:0.086059
[306] train-logloss:0.005484 eval-logloss:0.085971
[307] train-logloss:0.005478 eval-logloss:0.085998
[308] train-logloss:0.005471 eval-logloss:0.085998
[309] train-logloss:0.005464 eval-logloss:0.085877
[310] train-logloss:0.005457 eval-logloss:0.085923
[311] train-logloss:0.00545 eval-logloss:0.085948
[312] train-logloss:0.005444 eval-logloss:0.086028
[313] train-logloss:0.005437 eval-logloss:0.086112
[314] train-logloss:0.00543 eval-logloss:0.085989
[315] train-logloss:0.005424 eval-logloss:0.085903
[316] train-logloss:0.005417 eval-logloss:0.085949
[317] train-logloss:0.005411 eval-logloss:0.085977
[318] train-logloss:0.005404 eval-logloss:0.086002
[319] train-logloss:0.005398 eval-logloss:0.085883
[320] train-logloss:0.005392 eval-logloss:0.085967
[321] train-logloss:0.005385 eval-logloss:0.086046
[322] train-logloss:0.005379 eval-logloss:0.086091
[323] train-logloss:0.005373 eval-logloss:0.085977
[324] train-logloss:0.005366 eval-logloss:0.085978
[325] train-logloss:0.00536 eval-logloss:0.085896
[326] train-logloss:0.005354 eval-logloss:0.08578
[327] train-logloss:0.005348 eval-logloss:0.085857
[328] train-logloss:0.005342 eval-logloss:0.085939
[329] train-logloss:0.005336 eval-logloss:0.085825
[330] train-logloss:0.00533 eval-logloss:0.085869
[331] train-logloss:0.005324 eval-logloss:0.085893
[332] train-logloss:0.005318 eval-logloss:0.085922
[333] train-logloss:0.005312 eval-logloss:0.085842
[334] train-logloss:0.005306 eval-logloss:0.085735
[335] train-logloss:0.0053 eval-logloss:0.085816
[336] train-logloss:0.005294 eval-logloss:0.085892
[337] train-logloss:0.005288 eval-logloss:0.085936
[338] train-logloss:0.005283 eval-logloss:0.08583
[339] train-logloss:0.005277 eval-logloss:0.085909
[340] train-logloss:0.005271 eval-logloss:0.085831
[341] train-logloss:0.005265 eval-logloss:0.085727
[342] train-logloss:0.00526 eval-logloss:0.085678
[343] train-logloss:0.005254 eval-logloss:0.085721
[344] train-logloss:0.005249 eval-logloss:0.085796
[345] train-logloss:0.005243 eval-logloss:0.085819
[346] train-logloss:0.005237 eval-logloss:0.085715
[347] train-logloss:0.005232 eval-logloss:0.085793
[348] train-logloss:0.005227 eval-logloss:0.085835
[349] train-logloss:0.005221 eval-logloss:0.085734
[350] train-logloss:0.005216 eval-logloss:0.085658
[351] train-logloss:0.00521 eval-logloss:0.08573
[352] train-logloss:0.005205 eval-logloss:0.085807
[353] train-logloss:0.0052 eval-logloss:0.085706
[354] train-logloss:0.005195 eval-logloss:0.085659
[355] train-logloss:0.005189 eval-logloss:0.085701
[356] train-logloss:0.005184 eval-logloss:0.085628
[357] train-logloss:0.005179 eval-logloss:0.085529
[358] train-logloss:0.005174 eval-logloss:0.085604
[359] train-logloss:0.005169 eval-logloss:0.085676
[360] train-logloss:0.005164 eval-logloss:0.085579
[361] train-logloss:0.005159 eval-logloss:0.085601
[362] train-logloss:0.005153 eval-logloss:0.085643
[363] train-logloss:0.005149 eval-logloss:0.085713
[364] train-logloss:0.005144 eval-logloss:0.085787
[365] train-logloss:0.005139 eval-logloss:0.085689
[366] train-logloss:0.005134 eval-logloss:0.08573
[367] train-logloss:0.005129 eval-logloss:0.085684
[368] train-logloss:0.005124 eval-logloss:0.085589
[369] train-logloss:0.005119 eval-logloss:0.085516
[370] train-logloss:0.005114 eval-logloss:0.085588
[371] train-logloss:0.00511 eval-logloss:0.085495
[372] train-logloss:0.005105 eval-logloss:0.085564
[373] train-logloss:0.0051 eval-logloss:0.085605
[374] train-logloss:0.005096 eval-logloss:0.085626
[375] train-logloss:0.005091 eval-logloss:0.085535
[376] train-logloss:0.005086 eval-logloss:0.085606
[377] train-logloss:0.005082 eval-logloss:0.085674
[378] train-logloss:0.005077 eval-logloss:0.085714
[379] train-logloss:0.005073 eval-logloss:0.085624
[380] train-logloss:0.005068 eval-logloss:0.085579
[381] train-logloss:0.005064 eval-logloss:0.085619
[382] train-logloss:0.00506 eval-logloss:0.085638
[383] train-logloss:0.005055 eval-logloss:0.08555
[384] train-logloss:0.005051 eval-logloss:0.085617
[385] train-logloss:0.005047 eval-logloss:0.085621
[386] train-logloss:0.005042 eval-logloss:0.085551
[387] train-logloss:0.005038 eval-logloss:0.085463
[388] train-logloss:0.005034 eval-logloss:0.085502
[389] train-logloss:0.005029 eval-logloss:0.085459
[390] train-logloss:0.005025 eval-logloss:0.085321
[391] train-logloss:0.005021 eval-logloss:0.085389
[392] train-logloss:0.005017 eval-logloss:0.085303
[393] train-logloss:0.005013 eval-logloss:0.085369
[394] train-logloss:0.005009 eval-logloss:0.085301
[395] train-logloss:0.005005 eval-logloss:0.085368
[396] train-logloss:0.005 eval-logloss:0.085283
[397] train-logloss:0.004996 eval-logloss:0.08532
[398] train-logloss:0.004992 eval-logloss:0.085279
[399] train-logloss:0.004988 eval-logloss:0.085196
predict()를 통해 예측 확률값을 반환하고 예측 값으로 변환
pred_probs = xgb_model.predict(dtest)
print('predict( ) 수행 결과값을 10개만 표시, 예측 확률 값으로 표시됨')
print(np.round(pred_probs[:10],3))
# 예측 확률이 0.5 보다 크면 1 , 그렇지 않으면 0 으로 예측값 결정하여 List 객체인 preds에 저장
preds = [ 1 if x > 0.5 else 0 for x in pred_probs ]
print('예측값 10개만 표시:',preds[:10])
# predict( ) 수행 결과값을 10개만 표시, 예측 확률 값으로 표시됨
# [0.95 0.003 0.9 0.086 0.993 1. 1. 0.999 0.998 0. ]
# 예측값 10개만 표시: [1, 0, 1, 0, 1, 1, 1, 1, 1, 0]
get_clf_eval( )을 통해 예측 평가
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.metrics import precision_score, recall_score
from sklearn.metrics import f1_score, roc_auc_score
# 수정된 get_clf_eval() 함수
def get_clf_eval(y_test, pred=None, pred_proba=None):
confusion = confusion_matrix( y_test, pred)
accuracy = accuracy_score(y_test , pred)
precision = precision_score(y_test , pred)
recall = recall_score(y_test , pred)
f1 = f1_score(y_test,pred)
# ROC-AUC 추가
roc_auc = roc_auc_score(y_test, pred_proba)
print('오차 행렬')
print(confusion)
# ROC-AUC print 추가
print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\
F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))
get_clf_eval(y_test , preds, pred_probs)
오차 행렬
[[35 2]
[ 1 76]]
정확도: 0.9737, 정밀도: 0.9744, 재현율: 0.9870, F1: 0.9806, AUC:0.9951
Feature Importance 시각화
import matplotlib.pyplot as plt
%matplotlib inline
fig, ax = plt.subplots(figsize=(10, 12))
plot_importance(xgb_model, ax=ax)
사이킷런 Wrapper XGBoost 개요 및 적용
** 사이킷런 래퍼 클래스 임포트, 학습 및 예측
# 사이킷런 래퍼 XGBoost 클래스인 XGBClassifier 임포트
from xgboost import XGBClassifier
evals = [(X_test, y_test)]
xgb_wrapper = XGBClassifier(n_estimators=400, learning_rate=0.1, max_depth=3)
xgb_wrapper.fit(X_train , y_train, early_stopping_rounds=400,eval_set=evals, eval_metric="logloss", verbose=True)
w_preds = xgb_wrapper.predict(X_test)
w_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]
[0] validation_0-logloss:0.61352
Will train until validation_0-logloss hasn't improved in 400 rounds.
[1] validation_0-logloss:0.547842
[2] validation_0-logloss:0.494248
[3] validation_0-logloss:0.447986
[4] validation_0-logloss:0.409109
[5] validation_0-logloss:0.374977
[6] validation_0-logloss:0.345714
[7] validation_0-logloss:0.320529
[8] validation_0-logloss:0.29721
[9] validation_0-logloss:0.277991
[10] validation_0-logloss:0.260302
[11] validation_0-logloss:0.246037
[12] validation_0-logloss:0.231556
[13] validation_0-logloss:0.22005
[14] validation_0-logloss:0.208572
[15] validation_0-logloss:0.199993
[16] validation_0-logloss:0.190118
[17] validation_0-logloss:0.181818
[18] validation_0-logloss:0.174729
[19] validation_0-logloss:0.167657
[20] validation_0-logloss:0.158202
[21] validation_0-logloss:0.154725
[22] validation_0-logloss:0.148947
[23] validation_0-logloss:0.143308
[24] validation_0-logloss:0.136344
[25] validation_0-logloss:0.132778
[26] validation_0-logloss:0.127912
[27] validation_0-logloss:0.125263
[28] validation_0-logloss:0.119978
[29] validation_0-logloss:0.116412
[30] validation_0-logloss:0.114502
[31] validation_0-logloss:0.112572
[32] validation_0-logloss:0.11154
[33] validation_0-logloss:0.108681
[34] validation_0-logloss:0.106681
[35] validation_0-logloss:0.104207
[36] validation_0-logloss:0.102962
[37] validation_0-logloss:0.100576
[38] validation_0-logloss:0.098683
[39] validation_0-logloss:0.096444
[40] validation_0-logloss:0.095869
[41] validation_0-logloss:0.094242
[42] validation_0-logloss:0.094715
[43] validation_0-logloss:0.094272
[44] validation_0-logloss:0.093894
[45] validation_0-logloss:0.094184
[46] validation_0-logloss:0.09402
[47] validation_0-logloss:0.09236
[48] validation_0-logloss:0.093012
[49] validation_0-logloss:0.091273
[50] validation_0-logloss:0.090051
[51] validation_0-logloss:0.089605
[52] validation_0-logloss:0.089577
[53] validation_0-logloss:0.090703
[54] validation_0-logloss:0.089579
[55] validation_0-logloss:0.090357
[56] validation_0-logloss:0.091587
[57] validation_0-logloss:0.091527
[58] validation_0-logloss:0.091986
[59] validation_0-logloss:0.091951
[60] validation_0-logloss:0.091939
[61] validation_0-logloss:0.091461
[62] validation_0-logloss:0.090311
[63] validation_0-logloss:0.089407
[64] validation_0-logloss:0.089719
[65] validation_0-logloss:0.089743
[66] validation_0-logloss:0.089622
[67] validation_0-logloss:0.088734
[68] validation_0-logloss:0.088621
[69] validation_0-logloss:0.089739
[70] validation_0-logloss:0.089981
[71] validation_0-logloss:0.089782
[72] validation_0-logloss:0.089584
[73] validation_0-logloss:0.089533
[74] validation_0-logloss:0.088748
[75] validation_0-logloss:0.088597
[76] validation_0-logloss:0.08812
[77] validation_0-logloss:0.088396
[78] validation_0-logloss:0.088736
[79] validation_0-logloss:0.088153
[80] validation_0-logloss:0.087577
[81] validation_0-logloss:0.087412
[82] validation_0-logloss:0.08849
[83] validation_0-logloss:0.088575
[84] validation_0-logloss:0.08807
[85] validation_0-logloss:0.087641
[86] validation_0-logloss:0.087416
[87] validation_0-logloss:0.087611
[88] validation_0-logloss:0.087065
[89] validation_0-logloss:0.08727
[90] validation_0-logloss:0.087161
[91] validation_0-logloss:0.086962
[92] validation_0-logloss:0.087166
[93] validation_0-logloss:0.087067
[94] validation_0-logloss:0.086592
[95] validation_0-logloss:0.086116
[96] validation_0-logloss:0.087139
[97] validation_0-logloss:0.086768
[98] validation_0-logloss:0.086694
[99] validation_0-logloss:0.086547
[100] validation_0-logloss:0.086498
[101] validation_0-logloss:0.08641
[102] validation_0-logloss:0.086288
[103] validation_0-logloss:0.086258
[104] validation_0-logloss:0.086835
[105] validation_0-logloss:0.086767
[106] validation_0-logloss:0.087321
[107] validation_0-logloss:0.087304
[108] validation_0-logloss:0.08728
[109] validation_0-logloss:0.087298
[110] validation_0-logloss:0.087289
[111] validation_0-logloss:0.088002
[112] validation_0-logloss:0.087936
[113] validation_0-logloss:0.087843
[114] validation_0-logloss:0.088066
[115] validation_0-logloss:0.087649
[116] validation_0-logloss:0.087298
[117] validation_0-logloss:0.087799
[118] validation_0-logloss:0.087751
[119] validation_0-logloss:0.08768
[120] validation_0-logloss:0.087626
[121] validation_0-logloss:0.08757
[122] validation_0-logloss:0.087547
[123] validation_0-logloss:0.087156
[124] validation_0-logloss:0.08767
[125] validation_0-logloss:0.087737
[126] validation_0-logloss:0.088275
[127] validation_0-logloss:0.088309
[128] validation_0-logloss:0.088266
[129] validation_0-logloss:0.087886
[130] validation_0-logloss:0.088861
[131] validation_0-logloss:0.088675
[132] validation_0-logloss:0.088743
[133] validation_0-logloss:0.089218
[134] validation_0-logloss:0.089179
[135] validation_0-logloss:0.088821
[136] validation_0-logloss:0.088512
[137] validation_0-logloss:0.08848
[138] validation_0-logloss:0.088386
[139] validation_0-logloss:0.089145
[140] validation_0-logloss:0.08911
[141] validation_0-logloss:0.088765
[142] validation_0-logloss:0.088678
[143] validation_0-logloss:0.088389
[144] validation_0-logloss:0.089271
[145] validation_0-logloss:0.089238
[146] validation_0-logloss:0.089139
[147] validation_0-logloss:0.088907
[148] validation_0-logloss:0.089416
[149] validation_0-logloss:0.089388
[150] validation_0-logloss:0.089108
[151] validation_0-logloss:0.088735
[152] validation_0-logloss:0.088717
[153] validation_0-logloss:0.088484
[154] validation_0-logloss:0.088471
[155] validation_0-logloss:0.088545
[156] validation_0-logloss:0.088521
[157] validation_0-logloss:0.088547
[158] validation_0-logloss:0.088275
[159] validation_0-logloss:0.0883
[160] validation_0-logloss:0.08828
[161] validation_0-logloss:0.088013
[162] validation_0-logloss:0.087758
[163] validation_0-logloss:0.087784
[164] validation_0-logloss:0.087777
[165] validation_0-logloss:0.087517
[166] validation_0-logloss:0.087542
[167] validation_0-logloss:0.087642
[168] validation_0-logloss:0.08739
[169] validation_0-logloss:0.087377
[170] validation_0-logloss:0.087298
[171] validation_0-logloss:0.087368
[172] validation_0-logloss:0.087395
[173] validation_0-logloss:0.087385
[174] validation_0-logloss:0.087132
[175] validation_0-logloss:0.087159
[176] validation_0-logloss:0.086955
[177] validation_0-logloss:0.087053
[178] validation_0-logloss:0.08697
[179] validation_0-logloss:0.086973
[180] validation_0-logloss:0.087038
[181] validation_0-logloss:0.086799
[182] validation_0-logloss:0.086826
[183] validation_0-logloss:0.086582
[184] validation_0-logloss:0.086588
[185] validation_0-logloss:0.086614
[186] validation_0-logloss:0.086372
[187] validation_0-logloss:0.086369
[188] validation_0-logloss:0.086297
[189] validation_0-logloss:0.086104
[190] validation_0-logloss:0.086023
[191] validation_0-logloss:0.08605
[192] validation_0-logloss:0.086149
[193] validation_0-logloss:0.085916
[194] validation_0-logloss:0.085915
[195] validation_0-logloss:0.085984
[196] validation_0-logloss:0.086012
[197] validation_0-logloss:0.085922
[198] validation_0-logloss:0.085853
[199] validation_0-logloss:0.085874
[200] validation_0-logloss:0.085888
[201] validation_0-logloss:0.08595
[202] validation_0-logloss:0.08573
[203] validation_0-logloss:0.08573
[204] validation_0-logloss:0.085753
[205] validation_0-logloss:0.085821
[206] validation_0-logloss:0.08584
[207] validation_0-logloss:0.085776
[208] validation_0-logloss:0.085686
[209] validation_0-logloss:0.08571
[210] validation_0-logloss:0.085806
[211] validation_0-logloss:0.085593
[212] validation_0-logloss:0.085801
[213] validation_0-logloss:0.085807
[214] validation_0-logloss:0.085744
[215] validation_0-logloss:0.085658
[216] validation_0-logloss:0.085843
[217] validation_0-logloss:0.085632
[218] validation_0-logloss:0.085726
[219] validation_0-logloss:0.085783
[220] validation_0-logloss:0.085791
[221] validation_0-logloss:0.085817
[222] validation_0-logloss:0.085757
[223] validation_0-logloss:0.085674
[224] validation_0-logloss:0.08586
[225] validation_0-logloss:0.085871
[226] validation_0-logloss:0.085927
[227] validation_0-logloss:0.085954
[228] validation_0-logloss:0.085874
[229] validation_0-logloss:0.086057
[230] validation_0-logloss:0.086002
[231] validation_0-logloss:0.085922
[232] validation_0-logloss:0.086102
[233] validation_0-logloss:0.086115
[234] validation_0-logloss:0.086169
[235] validation_0-logloss:0.086263
[236] validation_0-logloss:0.086291
[237] validation_0-logloss:0.086217
[238] validation_0-logloss:0.086395
[239] validation_0-logloss:0.086342
[240] validation_0-logloss:0.08618
[241] validation_0-logloss:0.086195
[242] validation_0-logloss:0.086248
[243] validation_0-logloss:0.086263
[244] validation_0-logloss:0.086293
[245] validation_0-logloss:0.086222
[246] validation_0-logloss:0.086398
[247] validation_0-logloss:0.086347
[248] validation_0-logloss:0.086276
[249] validation_0-logloss:0.086448
[250] validation_0-logloss:0.086294
[251] validation_0-logloss:0.086312
[252] validation_0-logloss:0.086364
[253] validation_0-logloss:0.086394
[254] validation_0-logloss:0.08649
[255] validation_0-logloss:0.086441
[256] validation_0-logloss:0.08629
[257] validation_0-logloss:0.086459
[258] validation_0-logloss:0.086391
[259] validation_0-logloss:0.086441
[260] validation_0-logloss:0.086461
[261] validation_0-logloss:0.086491
[262] validation_0-logloss:0.086445
[263] validation_0-logloss:0.086466
[264] validation_0-logloss:0.086319
[265] validation_0-logloss:0.086488
[266] validation_0-logloss:0.086538
[267] validation_0-logloss:0.086471
[268] validation_0-logloss:0.086501
[269] validation_0-logloss:0.086522
[270] validation_0-logloss:0.086689
[271] validation_0-logloss:0.086738
[272] validation_0-logloss:0.086829
[273] validation_0-logloss:0.086684
[274] validation_0-logloss:0.08664
[275] validation_0-logloss:0.086496
[276] validation_0-logloss:0.086355
[277] validation_0-logloss:0.086519
[278] validation_0-logloss:0.086567
[279] validation_0-logloss:0.08659
[280] validation_0-logloss:0.086679
[281] validation_0-logloss:0.086637
[282] validation_0-logloss:0.086499
[283] validation_0-logloss:0.086356
[284] validation_0-logloss:0.086405
[285] validation_0-logloss:0.086429
[286] validation_0-logloss:0.086456
[287] validation_0-logloss:0.086504
[288] validation_0-logloss:0.08637
[289] validation_0-logloss:0.086457
[290] validation_0-logloss:0.086453
[291] validation_0-logloss:0.086322
[292] validation_0-logloss:0.086284
[293] validation_0-logloss:0.086148
[294] validation_0-logloss:0.086196
[295] validation_0-logloss:0.086221
[296] validation_0-logloss:0.086308
[297] validation_0-logloss:0.086178
[298] validation_0-logloss:0.086263
[299] validation_0-logloss:0.086131
[300] validation_0-logloss:0.086179
[301] validation_0-logloss:0.086052
[302] validation_0-logloss:0.086016
[303] validation_0-logloss:0.086101
[304] validation_0-logloss:0.085977
[305] validation_0-logloss:0.086059
[306] validation_0-logloss:0.085971
[307] validation_0-logloss:0.085998
[308] validation_0-logloss:0.085998
[309] validation_0-logloss:0.085877
[310] validation_0-logloss:0.085923
[311] validation_0-logloss:0.085948
[312] validation_0-logloss:0.086028
[313] validation_0-logloss:0.086112
[314] validation_0-logloss:0.085989
[315] validation_0-logloss:0.085903
[316] validation_0-logloss:0.085949
[317] validation_0-logloss:0.085977
[318] validation_0-logloss:0.086002
[319] validation_0-logloss:0.085883
[320] validation_0-logloss:0.085967
[321] validation_0-logloss:0.086046
[322] validation_0-logloss:0.086091
[323] validation_0-logloss:0.085977
[324] validation_0-logloss:0.085978
[325] validation_0-logloss:0.085896
[326] validation_0-logloss:0.08578
[327] validation_0-logloss:0.085857
[328] validation_0-logloss:0.085939
[329] validation_0-logloss:0.085825
[330] validation_0-logloss:0.085869
[331] validation_0-logloss:0.085893
[332] validation_0-logloss:0.085922
[333] validation_0-logloss:0.085842
[334] validation_0-logloss:0.085735
[335] validation_0-logloss:0.085816
[336] validation_0-logloss:0.085892
[337] validation_0-logloss:0.085936
[338] validation_0-logloss:0.08583
[339] validation_0-logloss:0.085909
[340] validation_0-logloss:0.085831
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[342] validation_0-logloss:0.085678
[343] validation_0-logloss:0.085721
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[345] validation_0-logloss:0.085819
[346] validation_0-logloss:0.085715
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[348] validation_0-logloss:0.085835
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[350] validation_0-logloss:0.085658
[351] validation_0-logloss:0.08573
[352] validation_0-logloss:0.085807
[353] validation_0-logloss:0.085706
[354] validation_0-logloss:0.085659
[355] validation_0-logloss:0.085701
[356] validation_0-logloss:0.085628
[357] validation_0-logloss:0.085529
[358] validation_0-logloss:0.085604
[359] validation_0-logloss:0.085676
[360] validation_0-logloss:0.085579
[361] validation_0-logloss:0.085601
[362] validation_0-logloss:0.085643
[363] validation_0-logloss:0.085713
[364] validation_0-logloss:0.085787
[365] validation_0-logloss:0.085689
[366] validation_0-logloss:0.08573
[367] validation_0-logloss:0.085684
[368] validation_0-logloss:0.085589
[369] validation_0-logloss:0.085516
[370] validation_0-logloss:0.085588
[371] validation_0-logloss:0.085495
[372] validation_0-logloss:0.085564
[373] validation_0-logloss:0.085605
[374] validation_0-logloss:0.085626
[375] validation_0-logloss:0.085535
[376] validation_0-logloss:0.085606
[377] validation_0-logloss:0.085674
[378] validation_0-logloss:0.085714
[379] validation_0-logloss:0.085624
[380] validation_0-logloss:0.085579
[381] validation_0-logloss:0.085619
[382] validation_0-logloss:0.085638
[383] validation_0-logloss:0.08555
[384] validation_0-logloss:0.085617
[385] validation_0-logloss:0.085621
[386] validation_0-logloss:0.085551
[387] validation_0-logloss:0.085463
[388] validation_0-logloss:0.085502
[389] validation_0-logloss:0.085459
[390] validation_0-logloss:0.085321
[391] validation_0-logloss:0.085389
[392] validation_0-logloss:0.085303
[393] validation_0-logloss:0.085369
[394] validation_0-logloss:0.085301
[395] validation_0-logloss:0.085368
[396] validation_0-logloss:0.085283
[397] validation_0-logloss:0.08532
[398] validation_0-logloss:0.085279
[399] validation_0-logloss:0.085196
get_clf_eval(y_test , w_preds, w_pred_proba)
오차 행렬
[[35 2]
[ 1 76]]
정확도: 0.9737, 정밀도: 0.9744, 재현율: 0.9870, F1: 0.9806, AUC:0.9951
early stopping을 100으로 설정하고 재 학습/예측/평가
from xgboost import XGBClassifier
xgb_wrapper = XGBClassifier(n_estimators=400, learning_rate=0.1, max_depth=3)
evals = [(X_test, y_test)]
xgb_wrapper.fit(X_train, y_train, early_stopping_rounds=100, eval_metric="logloss",
eval_set=evals, verbose=True)
ws100_preds = xgb_wrapper.predict(X_test)
ws100_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]
[0] validation_0-logloss:0.61352
Will train until validation_0-logloss hasn't improved in 100 rounds.
[1] validation_0-logloss:0.547842
[2] validation_0-logloss:0.494248
[3] validation_0-logloss:0.447986
[4] validation_0-logloss:0.409109
[5] validation_0-logloss:0.374977
[6] validation_0-logloss:0.345714
[7] validation_0-logloss:0.320529
[8] validation_0-logloss:0.29721
[9] validation_0-logloss:0.277991
[10] validation_0-logloss:0.260302
[11] validation_0-logloss:0.246037
[12] validation_0-logloss:0.231556
[13] validation_0-logloss:0.22005
[14] validation_0-logloss:0.208572
[15] validation_0-logloss:0.199993
[16] validation_0-logloss:0.190118
[17] validation_0-logloss:0.181818
[18] validation_0-logloss:0.174729
[19] validation_0-logloss:0.167657
[20] validation_0-logloss:0.158202
[21] validation_0-logloss:0.154725
[22] validation_0-logloss:0.148947
[23] validation_0-logloss:0.143308
[24] validation_0-logloss:0.136344
[25] validation_0-logloss:0.132778
[26] validation_0-logloss:0.127912
[27] validation_0-logloss:0.125263
[28] validation_0-logloss:0.119978
[29] validation_0-logloss:0.116412
[30] validation_0-logloss:0.114502
[31] validation_0-logloss:0.112572
[32] validation_0-logloss:0.11154
[33] validation_0-logloss:0.108681
[34] validation_0-logloss:0.106681
[35] validation_0-logloss:0.104207
[36] validation_0-logloss:0.102962
[37] validation_0-logloss:0.100576
[38] validation_0-logloss:0.098683
[39] validation_0-logloss:0.096444
[40] validation_0-logloss:0.095869
[41] validation_0-logloss:0.094242
[42] validation_0-logloss:0.094715
[43] validation_0-logloss:0.094272
[44] validation_0-logloss:0.093894
[45] validation_0-logloss:0.094184
[46] validation_0-logloss:0.09402
[47] validation_0-logloss:0.09236
[48] validation_0-logloss:0.093012
[49] validation_0-logloss:0.091273
[50] validation_0-logloss:0.090051
[51] validation_0-logloss:0.089605
[52] validation_0-logloss:0.089577
[53] validation_0-logloss:0.090703
[54] validation_0-logloss:0.089579
[55] validation_0-logloss:0.090357
[56] validation_0-logloss:0.091587
[57] validation_0-logloss:0.091527
[58] validation_0-logloss:0.091986
[59] validation_0-logloss:0.091951
[60] validation_0-logloss:0.091939
[61] validation_0-logloss:0.091461
[62] validation_0-logloss:0.090311
[63] validation_0-logloss:0.089407
[64] validation_0-logloss:0.089719
[65] validation_0-logloss:0.089743
[66] validation_0-logloss:0.089622
[67] validation_0-logloss:0.088734
[68] validation_0-logloss:0.088621
[69] validation_0-logloss:0.089739
[70] validation_0-logloss:0.089981
[71] validation_0-logloss:0.089782
[72] validation_0-logloss:0.089584
[73] validation_0-logloss:0.089533
[74] validation_0-logloss:0.088748
[75] validation_0-logloss:0.088597
[76] validation_0-logloss:0.08812
[77] validation_0-logloss:0.088396
[78] validation_0-logloss:0.088736
[79] validation_0-logloss:0.088153
[80] validation_0-logloss:0.087577
[81] validation_0-logloss:0.087412
[82] validation_0-logloss:0.08849
[83] validation_0-logloss:0.088575
[84] validation_0-logloss:0.08807
[85] validation_0-logloss:0.087641
[86] validation_0-logloss:0.087416
[87] validation_0-logloss:0.087611
[88] validation_0-logloss:0.087065
[89] validation_0-logloss:0.08727
[90] validation_0-logloss:0.087161
[91] validation_0-logloss:0.086962
[92] validation_0-logloss:0.087166
[93] validation_0-logloss:0.087067
[94] validation_0-logloss:0.086592
[95] validation_0-logloss:0.086116
[96] validation_0-logloss:0.087139
[97] validation_0-logloss:0.086768
[98] validation_0-logloss:0.086694
[99] validation_0-logloss:0.086547
[100] validation_0-logloss:0.086498
[101] validation_0-logloss:0.08641
[102] validation_0-logloss:0.086288
[103] validation_0-logloss:0.086258
[104] validation_0-logloss:0.086835
[105] validation_0-logloss:0.086767
[106] validation_0-logloss:0.087321
[107] validation_0-logloss:0.087304
[108] validation_0-logloss:0.08728
[109] validation_0-logloss:0.087298
[110] validation_0-logloss:0.087289
[111] validation_0-logloss:0.088002
[112] validation_0-logloss:0.087936
[113] validation_0-logloss:0.087843
[114] validation_0-logloss:0.088066
[115] validation_0-logloss:0.087649
[116] validation_0-logloss:0.087298
[117] validation_0-logloss:0.087799
[118] validation_0-logloss:0.087751
[119] validation_0-logloss:0.08768
[120] validation_0-logloss:0.087626
[121] validation_0-logloss:0.08757
[122] validation_0-logloss:0.087547
[123] validation_0-logloss:0.087156
[124] validation_0-logloss:0.08767
[125] validation_0-logloss:0.087737
[126] validation_0-logloss:0.088275
[127] validation_0-logloss:0.088309
[128] validation_0-logloss:0.088266
[129] validation_0-logloss:0.087886
[130] validation_0-logloss:0.088861
[131] validation_0-logloss:0.088675
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[133] validation_0-logloss:0.089218
[134] validation_0-logloss:0.089179
[135] validation_0-logloss:0.088821
[136] validation_0-logloss:0.088512
[137] validation_0-logloss:0.08848
[138] validation_0-logloss:0.088386
[139] validation_0-logloss:0.089145
[140] validation_0-logloss:0.08911
[141] validation_0-logloss:0.088765
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[143] validation_0-logloss:0.088389
[144] validation_0-logloss:0.089271
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[148] validation_0-logloss:0.089416
[149] validation_0-logloss:0.089388
[150] validation_0-logloss:0.089108
[151] validation_0-logloss:0.088735
[152] validation_0-logloss:0.088717
[153] validation_0-logloss:0.088484
[154] validation_0-logloss:0.088471
[155] validation_0-logloss:0.088545
[156] validation_0-logloss:0.088521
[157] validation_0-logloss:0.088547
[158] validation_0-logloss:0.088275
[159] validation_0-logloss:0.0883
[160] validation_0-logloss:0.08828
[161] validation_0-logloss:0.088013
[162] validation_0-logloss:0.087758
[163] validation_0-logloss:0.087784
[164] validation_0-logloss:0.087777
[165] validation_0-logloss:0.087517
[166] validation_0-logloss:0.087542
[167] validation_0-logloss:0.087642
[168] validation_0-logloss:0.08739
[169] validation_0-logloss:0.087377
[170] validation_0-logloss:0.087298
[171] validation_0-logloss:0.087368
[172] validation_0-logloss:0.087395
[173] validation_0-logloss:0.087385
[174] validation_0-logloss:0.087132
[175] validation_0-logloss:0.087159
[176] validation_0-logloss:0.086955
[177] validation_0-logloss:0.087053
[178] validation_0-logloss:0.08697
[179] validation_0-logloss:0.086973
[180] validation_0-logloss:0.087038
[181] validation_0-logloss:0.086799
[182] validation_0-logloss:0.086826
[183] validation_0-logloss:0.086582
[184] validation_0-logloss:0.086588
[185] validation_0-logloss:0.086614
[186] validation_0-logloss:0.086372
[187] validation_0-logloss:0.086369
[188] validation_0-logloss:0.086297
[189] validation_0-logloss:0.086104
[190] validation_0-logloss:0.086023
[191] validation_0-logloss:0.08605
[192] validation_0-logloss:0.086149
[193] validation_0-logloss:0.085916
[194] validation_0-logloss:0.085915
[195] validation_0-logloss:0.085984
[196] validation_0-logloss:0.086012
[197] validation_0-logloss:0.085922
[198] validation_0-logloss:0.085853
[199] validation_0-logloss:0.085874
[200] validation_0-logloss:0.085888
[201] validation_0-logloss:0.08595
[202] validation_0-logloss:0.08573
[203] validation_0-logloss:0.08573
[204] validation_0-logloss:0.085753
[205] validation_0-logloss:0.085821
[206] validation_0-logloss:0.08584
[207] validation_0-logloss:0.085776
[208] validation_0-logloss:0.085686
[209] validation_0-logloss:0.08571
[210] validation_0-logloss:0.085806
[211] validation_0-logloss:0.085593
[212] validation_0-logloss:0.085801
[213] validation_0-logloss:0.085807
[214] validation_0-logloss:0.085744
[215] validation_0-logloss:0.085658
[216] validation_0-logloss:0.085843
[217] validation_0-logloss:0.085632
[218] validation_0-logloss:0.085726
[219] validation_0-logloss:0.085783
[220] validation_0-logloss:0.085791
[221] validation_0-logloss:0.085817
[222] validation_0-logloss:0.085757
[223] validation_0-logloss:0.085674
[224] validation_0-logloss:0.08586
[225] validation_0-logloss:0.085871
[226] validation_0-logloss:0.085927
[227] validation_0-logloss:0.085954
[228] validation_0-logloss:0.085874
[229] validation_0-logloss:0.086057
[230] validation_0-logloss:0.086002
[231] validation_0-logloss:0.085922
[232] validation_0-logloss:0.086102
[233] validation_0-logloss:0.086115
[234] validation_0-logloss:0.086169
[235] validation_0-logloss:0.086263
[236] validation_0-logloss:0.086291
[237] validation_0-logloss:0.086217
[238] validation_0-logloss:0.086395
[239] validation_0-logloss:0.086342
[240] validation_0-logloss:0.08618
[241] validation_0-logloss:0.086195
[242] validation_0-logloss:0.086248
[243] validation_0-logloss:0.086263
[244] validation_0-logloss:0.086293
[245] validation_0-logloss:0.086222
[246] validation_0-logloss:0.086398
[247] validation_0-logloss:0.086347
[248] validation_0-logloss:0.086276
[249] validation_0-logloss:0.086448
[250] validation_0-logloss:0.086294
[251] validation_0-logloss:0.086312
[252] validation_0-logloss:0.086364
[253] validation_0-logloss:0.086394
[254] validation_0-logloss:0.08649
[255] validation_0-logloss:0.086441
[256] validation_0-logloss:0.08629
[257] validation_0-logloss:0.086459
[258] validation_0-logloss:0.086391
[259] validation_0-logloss:0.086441
[260] validation_0-logloss:0.086461
[261] validation_0-logloss:0.086491
[262] validation_0-logloss:0.086445
[263] validation_0-logloss:0.086466
[264] validation_0-logloss:0.086319
[265] validation_0-logloss:0.086488
[266] validation_0-logloss:0.086538
[267] validation_0-logloss:0.086471
[268] validation_0-logloss:0.086501
[269] validation_0-logloss:0.086522
[270] validation_0-logloss:0.086689
[271] validation_0-logloss:0.086738
[272] validation_0-logloss:0.086829
[273] validation_0-logloss:0.086684
[274] validation_0-logloss:0.08664
[275] validation_0-logloss:0.086496
[276] validation_0-logloss:0.086355
[277] validation_0-logloss:0.086519
[278] validation_0-logloss:0.086567
[279] validation_0-logloss:0.08659
[280] validation_0-logloss:0.086679
[281] validation_0-logloss:0.086637
[282] validation_0-logloss:0.086499
[283] validation_0-logloss:0.086356
[284] validation_0-logloss:0.086405
[285] validation_0-logloss:0.086429
[286] validation_0-logloss:0.086456
[287] validation_0-logloss:0.086504
[288] validation_0-logloss:0.08637
[289] validation_0-logloss:0.086457
[290] validation_0-logloss:0.086453
[291] validation_0-logloss:0.086322
[292] validation_0-logloss:0.086284
[293] validation_0-logloss:0.086148
[294] validation_0-logloss:0.086196
[295] validation_0-logloss:0.086221
[296] validation_0-logloss:0.086308
[297] validation_0-logloss:0.086178
[298] validation_0-logloss:0.086263
[299] validation_0-logloss:0.086131
[300] validation_0-logloss:0.086179
[301] validation_0-logloss:0.086052
[302] validation_0-logloss:0.086016
[303] validation_0-logloss:0.086101
[304] validation_0-logloss:0.085977
[305] validation_0-logloss:0.086059
[306] validation_0-logloss:0.085971
[307] validation_0-logloss:0.085998
[308] validation_0-logloss:0.085998
[309] validation_0-logloss:0.085877
[310] validation_0-logloss:0.085923
[311] validation_0-logloss:0.085948
Stopping. Best iteration:
[211] validation_0-logloss:0.085593
get_clf_eval(y_test , ws100_preds, ws100_pred_proba)
오차 행렬
[[35 2]
[ 1 76]]
정확도: 0.9737, 정밀도: 0.9744, 재현율: 0.9870, F1: 0.9806, AUC:0.9951
early stopping을 10으로 설정하고 재 학습/예측/평가
# early_stopping_rounds를 10으로 설정하고 재 학습.
xgb_wrapper.fit(X_train, y_train, early_stopping_rounds=10,
eval_metric="logloss", eval_set=evals,verbose=True)
ws10_preds = xgb_wrapper.predict(X_test)
ws10_pred_proba = xgb_wrapper.predict_proba(X_test)[:, 1]
[0] validation_0-logloss:0.61352
Will train until validation_0-logloss hasn't improved in 10 rounds.
[1] validation_0-logloss:0.547842
[2] validation_0-logloss:0.494248
[3] validation_0-logloss:0.447986
[4] validation_0-logloss:0.409109
[5] validation_0-logloss:0.374977
[6] validation_0-logloss:0.345714
[7] validation_0-logloss:0.320529
[8] validation_0-logloss:0.29721
[9] validation_0-logloss:0.277991
[10] validation_0-logloss:0.260302
[11] validation_0-logloss:0.246037
[12] validation_0-logloss:0.231556
[13] validation_0-logloss:0.22005
[14] validation_0-logloss:0.208572
[15] validation_0-logloss:0.199993
[16] validation_0-logloss:0.190118
[17] validation_0-logloss:0.181818
[18] validation_0-logloss:0.174729
[19] validation_0-logloss:0.167657
[20] validation_0-logloss:0.158202
[21] validation_0-logloss:0.154725
[22] validation_0-logloss:0.148947
[23] validation_0-logloss:0.143308
[24] validation_0-logloss:0.136344
[25] validation_0-logloss:0.132778
[26] validation_0-logloss:0.127912
[27] validation_0-logloss:0.125263
[28] validation_0-logloss:0.119978
[29] validation_0-logloss:0.116412
[30] validation_0-logloss:0.114502
[31] validation_0-logloss:0.112572
[32] validation_0-logloss:0.11154
[33] validation_0-logloss:0.108681
[34] validation_0-logloss:0.106681
[35] validation_0-logloss:0.104207
[36] validation_0-logloss:0.102962
[37] validation_0-logloss:0.100576
[38] validation_0-logloss:0.098683
[39] validation_0-logloss:0.096444
[40] validation_0-logloss:0.095869
[41] validation_0-logloss:0.094242
[42] validation_0-logloss:0.094715
[43] validation_0-logloss:0.094272
[44] validation_0-logloss:0.093894
[45] validation_0-logloss:0.094184
[46] validation_0-logloss:0.09402
[47] validation_0-logloss:0.09236
[48] validation_0-logloss:0.093012
[49] validation_0-logloss:0.091273
[50] validation_0-logloss:0.090051
[51] validation_0-logloss:0.089605
[52] validation_0-logloss:0.089577
[53] validation_0-logloss:0.090703
[54] validation_0-logloss:0.089579
[55] validation_0-logloss:0.090357
[56] validation_0-logloss:0.091587
[57] validation_0-logloss:0.091527
[58] validation_0-logloss:0.091986
[59] validation_0-logloss:0.091951
[60] validation_0-logloss:0.091939
[61] validation_0-logloss:0.091461
[62] validation_0-logloss:0.090311
Stopping. Best iteration:
[52] validation_0-logloss:0.089577
get_clf_eval(y_test , ws10_preds, ws10_pred_proba)
오차 행렬
[[34 3]
[ 2 75]]
정확도: 0.9561, 정밀도: 0.9615, 재현율: 0.9740, F1: 0.9677, AUC:0.9947
from xgboost import plot_importance
import matplotlib.pyplot as plt
%matplotlib inline
fig, ax = plt.subplots(figsize=(10, 12))
# 사이킷런 래퍼 클래스를 입력해도 무방.
plot_importance(xgb_wrapper, ax=ax)
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