해결된 질문
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기출4회 유형2 문의드립니다.
Macro f1-core는
#*****평가(=교차검증)
from sklearn.model_selection import cross_val_score
score = cross_val_score(model, train, train['Segmentation'], scoring='f1_macro', cv=5)
print(score)
print(score.mean())
이렇게 구하면 되는건가요?
그리고 아래와 같이 풀이해봤는데
강사님께서 풀이하신 segmentation과 다른데 괜찮나요?
풀이과정에 문제는 없는지 확인 부탁드립니다.
# 라이브러리 불러오기
import pandas as pd
# 데이터 불러오기
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
#*****데이터확인
train.shape, test.shape
train.head(2)
test.head(2)
#문자형 6개
# train.info()
#결측치 없음
train.isnull().sum()
test.isnull().sum()
#*****전처리
#결측값 없음
#train합치기 없음
#인코딩
from sklearn.preprocessing import LabelEncoder
cols= train.select_dtypes(include='object')
cols
for col in cols :
le = LabelEncoder()
train[col] = le.fit_transform(train[col])
test[col] = le.transform(test[col])
#id삭제
train = train.drop('ID',axis=1)
test_ = test.pop('ID')
#*****분리
from sklearn.model_selection import train_test_split
X_tr, X_val, y_tr, y_val = train_test_split(
train.drop('Segmentation',axis=1),
train['Segmentation'],
test_size=0.2,
random_state=2022
)
#*****모델 max_depth=5~7 / n_estimators= 100~1000
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=0, max_depth=7, n_estimators=500)
model.fit(X_tr, y_tr)
pred = model.predict(X_val)
#*****평가(=교차검증)
from sklearn.model_selection import cross_val_score
score = cross_val_score(model, train, train['Segmentation'], scoring='f1_macro', cv=5)
print(score)
print(score.mean())
#*****예측
pred = model.predict(test)
pred
submit = pd.DataFrame({
'ID': test_ID,
'Segmentation': pred
})
submit
#*****저장
submit.to_csv('submission_csv', index=False)
pd.read_csv('submission_csv')