上传文件至 /

function.py

@@ -38,7 +38,13 @@ def preprocess_data(X):
 
 def train_logreg(X, y):
     X_processed = preprocess_data(X)  # 预处理训练数据
-    model = RandomForestClassifier(n_estimators=200, max_depth=20, min_samples_split=5, random_state=42)
+    model = RandomForestClassifier(
+        n_estimators=300,  # 增加树的数量
+        max_depth=25,      # 调整最大深度
+        min_samples_split=5,
+        class_weight='balanced',  # 处理类别不平衡
+        random_state=42
+    )
     model.fit(X_processed, y)
     return model  # 返回训练好的模型
 
@@ -51,6 +57,7 @@ def predict_logreg(X, model):
 
 
 if __name__ == "__main__":
+    # 数据加载和预处理保持不变
     X, y = load_data("./data/horse-colic.data")
 
     # 划分训练集和测试集
@@ -64,4 +71,4 @@ if __name__ == "__main__":
 
     # 计算准确率
     accuracy = accuracy_score(y_test, y_pred)
-    print(f"Accuracy: {accuracy:.2f}")
+    print(f"Accuracy: {accuracy:.2f}")