04단계 · 2유형 — 예측과 제출
데이터 나누기 — 왜 나누나
`train_test_split` 으로 학습용과 검증용을 나누고, `stratify` 가 무엇을 맞추는지 본다.
먼저 알아볼게요
코드로 이동 ↓2유형은 라벨이 있는 학습 데이터로 모델을 만들고, 라벨이 없는 평가 데이터의 답을 예측해 제출하는 문제다. 평가 데이터의 정답은 우리에게 없다.
그래서 내 모델이 쓸 만한지 확인하려면, 학습 데이터를 다시 쪼개 일부를 "정답을 아는 가짜 시험지" 로 써야 한다. 이것을 검증(validation) 이라고 한다.
train_test_split(X, y, test_size=0.2, random_state=42) 는 20% 를 검증용으로 떼어 낸다. random_state 는 같은 방식으로 섞으라는 번호다. 이것을 고정해야 다시 돌렸을 때 같은 결과가 나온다.
결과는 순서가 정해져 있다: x_tr, x_va, y_tr, y_va. 순서를 바꿔 받으면 오류 없이 엉뚱한 학습이 된다.
stratify=y 를 주면 나눈 양쪽의 정답 비율을 원본에 맞춰 나눈다. 행 수가 딱 떨어지지 않으면 정확히 같아지지는 않고 가장 가깝게 맞춘다. 아래 과제에서는 random_state=7 로 그냥 나누면 학습 0.5547 · 검증 0.4375 로 어긋나는데, stratify=y 를 주면 이 데이터에서는 양쪽 다 원본과 같은 0.5312 가 된다.
이렇게 써요
import pandas as pd
from sklearn.model_selection import train_test_split
train = pd.read_csv("churn_train.csv")
X = train[["가입개월", "월요금"]]
y = train["이탈"]
a, b, c, d = train_test_split(X, y, test_size=0.5, random_state=0)
print(a.shape, b.shape)기억할 한 가지
random_state 를 고정해야 결과가 재현된다. 받는 순서는 x_tr, x_va, y_tr, y_va 다.
직접 해보기
churn_train.csv 에서 고객ID·이탈·성별·요금제 를 뺀 것을 X, 이탈 을 y 로 두고 ① random_state=42, test_size=0.2 로 나눈 두 X 의 shape ② 두 y 의 행 수 ③ 전체 y 의 평균(소수 4) ④ random_state=7 로 그냥 나눴을 때 두 y 의 평균 ⑤ 같은 random_state=7 에 stratify=y 를 더했을 때 두 y 의 평균을 출력하세요(모두 소수 4).
제공 파일 1개 · 내용 보기
파일은 준비되어 있어요. 코드에서 이름으로 불러오세요.
churn_train.csv
고객ID,성별,요금제,가입개월,월요금,부가서비스,이탈 C1001,남,standard,22,44300,3,1 C1002,여,premium,1,62000,1,1 C1003,남,premium,45,64000,0,1 C1004,남,basic,10,32200,1,1 C1005,남,premium,5,69500,0,1 C1006,여,basic,56,29500,0,0 C1007,여,premium,48,62800,2,0 C1008,여,standard,5,56700,1,1 C1009,여,basic,49,30600,3,0 C1010,여,premium,52,74800,2,1 C1011,여,basic,19,32800,2,1 C1012,여,standard,18,42200,3,0 C1013,남,standard,37,47900,1,0 C1014,남,basic,1,30500,4,1 C1015,여,standard,52,43200,0,0 C1016,남,premium,31,75300,3,1 C1017,여,basic,22,29100,1,1 C1018,남,standard,39,48400,1,1 C1019,남,premium,35,82500,0,0 C1020,남,standard,19,42600,3,0 C1021,남,basic,22,27300,4,1 C1022,여,basic,38,27800,1,1 C1023,남,standard,5,54200,0,1 C1024,여,premium,36,77200,3,0 C1025,여,standard,14,46500,1,1 C1026,남,premium,15,64500,2,1 C1027,남,basic,45,28100,4,0 C1028,남,standard,49,40600,1,1 C1029,남,standard,59,50800,4,0 C1030,여,basic,16,27800,3,1 C1031,남,basic,36,24900,0,1 C1032,남,basic,17,30200,4,0 C1033,여,basic,36,29900,3,1 C1034,남,basic,36,23800,2,0 C1035,여,standard,9,50200,1,1 C1036,여,premium,45,60800,2,0 C1037,여,standard,37,42900,3,0 C1038,여,basic,37,24800,2,0 C1039,남,standard,42,46300,0,0 C1040,여,basic,34,28400,2,1 C1041,남,premium,11,84200,3,1 C1042,남,standard,9,57000,4,1 C1043,여,standard,11,43500,3,1 C1044,여,basic,42,29200,2,1 C1045,여,premium,10,66800,4,1 C1046,남,premium,56,62700,2,0 C1047,남,standard,19,44100,3,1 C1048,여,basic,14,33700,2,0 C1049,여,basic,44,28800,0,1 C1050,여,premium,32,69800,0,1 C1051,남,basic,52,24100,3,0 C1052,남,standard,48,35900,1,0 C1053,여,basic,41,26300,0,0 C1054,여,basic,36,25500,1,1 C1055,여,basic,7,29000,3,0 C1056,여,basic,34,32700,3,1 C1057,남,standard,42,48900,4,1 C1058,여,basic,32,25800,1,1 C1059,남,standard,56,50100,3,0 C1060,여,standard,28,41500,1,0 C1061,여,premium,47,73900,0,0 C1062,남,premium,4,53900,3,1 C1063,여,premium,4,72600,0,1 C1064,여,basic,42,29100,4,1 C1065,남,basic,59,29800,4,0 C1066,남,premium,28,75100,1,0 C1067,남,basic,27,27200,0,0 C1068,남,basic,60,30100,2,1 C1069,여,premium,4,67400,1,1 C1070,여,basic,4,34100,0,1 C1071,남,standard,42,40800,4,0 C1072,여,premium,53,72900,4,0 C1073,남,standard,36,37500,1,0 C1074,남,standard,44,49800,0,0 C1075,남,basic,5,29700,3,1 C1076,남,premium,1,75600,3,1 C1077,남,standard,44,40600,1,1 C1078,여,basic,7,23800,1,0 C1079,여,premium,47,66100,4,1 C1080,남,standard,32,41500,1,0 C1081,남,premium,37,78500,4,1 C1082,남,premium,52,68500,1,0 C1083,남,premium,51,61900,3,1 C1084,남,basic,38,30000,2,1 C1085,남,basic,12,30900,0,0 C1086,남,premium,25,74900,1,1 C1087,여,standard,40,47300,1,0 C1088,남,standard,6,41600,1,1 C1089,남,premium,57,64200,4,0 C1090,여,basic,42,28700,0,1 C1091,여,standard,27,37300,0,1 C1092,남,basic,27,25900,2,1 C1093,여,basic,41,27900,0,0 C1094,남,standard,60,44200,0,0 C1095,남,premium,12,67100,2,1 C1096,남,standard,46,45700,1,0 C1097,여,standard,23,48800,1,1 C1098,남,premium,57,75300,4,0 C1099,남,premium,13,53900,0,0 C1100,여,premium,29,61600,3,1 C1101,남,standard,2,47300,3,1 C1102,남,standard,10,55900,1,1 C1103,남,premium,3,71800,1,1 C1104,남,premium,53,70000,1,0 C1105,남,premium,32,71300,2,1 C1106,남,premium,9,66000,0,1 C1107,여,premium,30,66100,2,1 C1108,남,standard,43,53500,3,0 C1109,남,basic,26,29400,4,0 C1110,여,standard,21,48500,4,0 C1111,남,standard,51,42400,0,1 C1112,남,premium,21,74100,4,0 C1113,남,premium,56,56600,0,0 C1114,남,standard,46,45300,4,0 C1115,여,standard,48,42300,4,0 C1116,여,basic,35,24700,4,1 C1117,여,premium,22,42700,1,0 C1118,남,standard,9,39900,4,1 C1119,남,premium,36,73100,1,0 C1120,여,premium,38,73800,0,1 C1121,여,standard,13,49300,4,0 C1122,남,basic,48,30300,0,0 C1123,남,standard,16,43800,0,1 C1124,남,premium,15,74000,2,1 C1125,남,basic,25,30200,0,1 C1126,남,basic,44,33000,2,1 C1127,남,basic,56,21800,3,0 C1128,남,standard,48,38600,1,0 C1129,여,premium,15,68200,0,1 C1130,여,basic,28,27400,3,0 C1131,남,standard,54,40800,4,0 C1132,남,basic,45,26900,3,0 C1133,여,premium,21,66200,0,1 C1134,여,premium,39,58900,0,0 C1135,남,basic,43,29000,3,0 C1136,남,standard,9,48600,4,1 C1137,남,basic,28,30300,1,0 C1138,남,premium,30,78600,4,0 C1139,남,basic,18,23900,3,0 C1140,남,standard,46,40900,2,1 C1141,남,premium,6,70300,0,1 C1142,남,basic,46,29100,1,0 C1143,남,standard,18,41600,3,0 C1144,여,basic,53,27800,3,1 C1145,여,basic,7,25400,0,1 C1146,남,basic,27,28800,0,1 C1147,여,premium,51,64300,4,0 C1148,남,premium,25,53100,2,0 C1149,남,standard,54,43800,1,1 C1150,남,premium,40,57700,0,1 C1151,여,standard,24,42900,3,1 C1152,남,premium,21,63300,1,1 C1153,여,standard,44,34800,3,0 C1154,여,basic,29,27400,2,1 C1155,여,premium,38,61500,1,0 C1156,여,premium,48,62600,4,0 C1157,남,basic,27,26900,1,1 C1158,남,standard,20,38000,4,0 C1159,여,premium,22,63700,2,1 C1160,여,standard,20,42800,1,0
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import pandas as pd
from sklearn.model_selection import train_test_split
train = pd.read_csv("churn_train.csv")
X = train.drop(columns=["고객ID", "이탈", "성별", "요금제"])
y = train["이탈"]
x_tr, x_va, y_tr, y_va = train_test_split(X, y, test_size=0.2, random_state=42)
print(x_tr.shape, x_va.shape)
print(y_tr.shape[0], y_va.shape[0])
print(round(y.mean(), 4))
a_tr, a_va, ay_tr, ay_va = train_test_split(X, y, test_size=0.2, random_state=7)
print(round(ay_tr.mean(), 4), round(ay_va.mean(), 4))
s_tr, s_va, sy_tr, sy_va = train_test_split(X, y, test_size=0.2, random_state=7, stratify=y)
print(round(sy_tr.mean(), 4), round(sy_va.mean(), 4))