04단계 · 2유형 — 예측과 제출
범주형 인코딩
특성의 범주형 열을 `OrdinalEncoder` 로 학습 기준에 맞춰 바꾸고, `get_dummies` 와 비교한다.
먼저 알아볼게요
코드로 이동 ↓모델은 숫자만 먹는다. "basic", "premium" 같은 글자 열은 숫자로 바꿔야 한다. 이것을 인코딩이라고 한다.
특성(X)의 범주형 열에는 `OrdinalEncoder` 를 쓴다. 값들을 정렬해 0, 1, 2 를 매기고, 그 순서가 enc.categories_ 에 열별로 들어 있다. 여러 열을 한 번에 넘길 수 있다.
LabelEncoder 라는 비슷한 도구가 있는데, 공식 문서상 그것은 타깃 `y` 전용이다. 실기 풀이에서 특성에도 관행적으로 쓰이지만, 한 번에 한 열만 받고 학습에 없던 값이 평가에 나오면 `transform` 이 오류로 멈춘다. OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1) 는 그 경우를 지정한 값으로 넘긴다.
규칙은 스케일러와 같다: 학습 데이터로 `fit_transform`, 평가 데이터는 `transform`. 각각 따로 fit_transform 하면 같은 글자가 다른 숫자가 되어 오류 없이 틀린다.
pd.get_dummies 는 값마다 0/1 열을 하나씩 만드는 원-핫 인코딩이다. 순서가 없는 범주에 더 정직하지만, 학습·평가의 열 구성이 달라질 수 있어 reindex(columns=학습열, fill_value=0) 로 맞춰 줘야 한다(model-rmsle 에서 실제로 쓴다). 무엇이 더 좋은지는 데이터와 모델에 달렸다.
이렇게 써요
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
train = pd.read_csv("churn_train.csv")
enc = OrdinalEncoder()
print(enc.fit_transform(train[["성별"]])[:8].ravel().tolist())
print([list(c) for c in enc.categories_])기억할 한 가지
특성의 범주형 열은 OrdinalEncoder(타깃은 LabelEncoder). 학습에서 fit_transform, 평가에서 transform 이다.
직접 해보기
churn_train.csv 와 churn_test.csv 의 성별·요금제 를 OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1) 로 학습 기준에 맞춰 바꾸고 ① categories_ 목록 ② 학습의 요금제 코드별 개수 ③ 평가의 요금제 코드별 개수를 출력한 뒤, 학습의 두 열을 get_dummies 로 바꿔 ④ 열 이름 목록 ⑤ 요금제_premium 의 합을 출력하세요.
제공 파일 2개 · 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
churn_test.csv
고객ID,성별,요금제,가입개월,월요금,부가서비스 C2001,남,basic,32,30800,2 C2002,남,premium,31,63600,0 C2003,여,premium,51,56500,1 C2004,남,basic,15,31600,0 C2005,남,basic,8,35800,1 C2006,남,premium,4,67100,1 C2007,여,basic,13,21200,1 C2008,남,premium,57,67200,4 C2009,남,standard,55,45400,0 C2010,남,standard,30,46700,3 C2011,남,standard,43,38400,4 C2012,남,basic,33,31700,0 C2013,여,basic,20,29900,1 C2014,남,standard,47,43100,2 C2015,남,basic,4,28100,3 C2016,여,basic,31,31000,4 C2017,여,basic,7,25000,1 C2018,남,standard,10,49900,3 C2019,여,premium,2,73800,4 C2020,여,standard,51,40200,0 C2021,여,premium,48,63300,0 C2022,남,premium,43,60000,0 C2023,여,standard,12,48400,1 C2024,남,premium,8,68400,0 C2025,여,standard,47,42900,4 C2026,여,premium,1,66400,4 C2027,여,basic,18,34800,0 C2028,남,basic,27,27400,0 C2029,남,standard,2,57500,4 C2030,남,premium,8,74400,4 C2031,남,premium,44,74600,0 C2032,남,basic,7,35500,4 C2033,남,premium,39,76400,0 C2034,여,basic,40,29700,1 C2035,여,standard,18,42100,1 C2036,여,standard,25,47700,4 C2037,여,premium,46,52700,4 C2038,남,premium,46,64700,2 C2039,여,premium,47,65700,1 C2040,여,basic,15,29500,2
내 코드 Python
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풀이와 비교하기
내 코드와 한 줄씩 비교해 보세요. 풀이를 보는 것만으로 완료되지는 않아요.
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
train = pd.read_csv("churn_train.csv")
test = pd.read_csv("churn_test.csv")
enc = OrdinalEncoder(handle_unknown="use_encoded_value", unknown_value=-1)
train[["성별_e", "요금제_e"]] = enc.fit_transform(train[["성별", "요금제"]])
test[["성별_e", "요금제_e"]] = enc.transform(test[["성별", "요금제"]])
print([list(c) for c in enc.categories_])
print(train["요금제_e"].value_counts().sort_index().tolist())
print(test["요금제_e"].value_counts().sort_index().tolist())
dummies = pd.get_dummies(train[["성별", "요금제"]])
print(dummies.columns.tolist())
print(int(dummies["요금제_premium"].sum()))