From aef68edb207342f1b989d425efb9128877be32db Mon Sep 17 00:00:00 2001
From: 202210423104 <1961532386@qq.com>
Date: Sun, 3 Nov 2024 15:40:49 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
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---
 function.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/function.py b/function.py
index 956ebcf..d62464f 100644
--- a/function.py
+++ b/function.py
@@ -30,12 +30,12 @@ def preprocess_data(X):
     X_poly = poly.fit_transform(X_standardized)
     return X_poly
 
-def train_random_forest(X, y):
+def train_logreg(X, y):
     model = RandomForestClassifier(n_estimators=113, max_depth=10, random_state=42)
     model.fit(X, y)
     return model
 
-def predict(model, X):
+def predict_logreg(model, X):
     return model.predict(X)
 
 if __name__ == "__main__":
@@ -51,14 +51,14 @@ if __name__ == "__main__":
     X_test_processed = preprocess_data(X_test)
 
     # 训练模型
-    model = train_random_forest(X_train_processed, y_train)
+    model = train_logreg(X_train_processed, y_train)
 
     # 交叉验证
     cv_scores = cross_val_score(model, X_train_processed, y_train, cv=5)
     print(f"交叉验证准确率: {np.mean(cv_scores) * 100:.2f}%")
 
     # 预测
-    y_pred = predict(model, X_test_processed)
+    y_pred = predict_logreg(model, X_test_processed)
 
     # 计算正确率
     accuracy = np.mean(y_pred == y_test)