更新 function.py

function.py

@@ -58,13 +58,13 @@ def load_data():
     X = data.drop(columns=["outcome"])  
     y = data["outcome"]  
   
-    # 划分训练集和测试集（这里简单按7:3划分，实际应用中应与原数据集划分保持一致）  
+    # 划分训练集和测试集（这里简单按7:3划分）  
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)  
   
     return X_train, X_test, y_train, y_test  
   
 def train_logreg(X_train, y_train):  
-    # 训练对率回归模型  
+    # 训练逻辑回归模型  
     model = LogisticRegression(max_iter=10000)  # 设置足够多的迭代次数以确保收敛  
     model.fit(X_train, y_train)  
     return model  
@@ -74,7 +74,7 @@ def predict_logreg(model, X_test):
     y_pred = model.predict(X_test)  
     return y_pred  
   
-# 示例调用（注：这些调用在自动评测时不会执行，仅供测试使用）  
+# 示例调用  
 if __name__ == "__main__":  
     X_train, X_test, y_train, y_test = load_data()  
     model = train_logreg(X_train, y_train)