From b3be0b4923f1249c39f6623fe2b0242c46e49509 Mon Sep 17 00:00:00 2001 From: jack <2170067548@qq.com> Date: Sun, 3 Nov 2024 17:26:32 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- function.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/function.py b/function.py index 61b706e..1002313 100644 --- a/function.py +++ b/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)