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질문&답변
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# 데이터 불러오기 train = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p4/4_2/train.csv") test = pd.read_csv("https://raw.githubusercontent.com/lovedlim/inf/refs/heads/main/p4/4_2/test.csv") train.info() # print(set(train['Gender']) == set(test['Gender'])) # print(set(train['Ever_Married']) == set(test['Ever_Married'])) # print(set(train['Graduated']) == set(test['Graduated'])) # print(set(train['Profession']) == set(test['Profession'])) # print(set(train['Spending_Score']) == set(test['Spending_Score'])) # print(set(train['Var_1']) == set(test['Var_1'])) target = train.pop("Segmentation") # train = train.drop('ID', axis=1) # test = test.drop('ID', axis=1) print(train.shape) print(test.shape) train = pd.get_dummies(train) test = pd.get_dummies(test) print(train.shape) print(test.shape) from sklearn.model_selection import train_test_split X_tr, X_val, y_tr, y_val = train_test_split(train, target, test_size=0.2, random_state=0) from sklearn.ensemble import RandomForestClassifier rf = RandomForestClassifier(random_state=0) rf.fit(X_tr, y_tr) pred_rf = rf.predict(X_val) import lightgbm as lgb lg = lgb.LGBMClassifier(verbose=-1, random_state=0) lg.fit(X_tr, y_tr) pred_lg = lg.predict(X_val) from sklearn.metrics import f1_score f1_rf = f1_score(y_val, pred_rf, average='macro') f1_lg = f1_score(y_val, pred_lg, average='macro') print(f1_rf) print(f1_lg)
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