From fc80ba65b7221cc79980e2b7802b88e323ac6bde Mon Sep 17 00:00:00 2001
From: 202210423104 <1961532386@qq.com>
Date: Sun, 3 Nov 2024 16:17:48 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 20 +++++---------------
 1 file changed, 5 insertions(+), 15 deletions(-)

diff --git a/function.py b/function.py
index 3b9e1c5..0e76974 100644
--- a/function.py
+++ b/function.py
@@ -1,7 +1,6 @@
 import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import PolynomialFeatures, StandardScaler
-from sklearn.model_selection import GridSearchCV
 
 id = "202210423104"
 name = "张钦诚"
@@ -26,26 +25,17 @@ def load_data(filepath):
 def preprocess_data(X):
     scaler = StandardScaler()
     X_standardized = scaler.fit_transform(X)
-    poly = PolynomialFeatures(degree=3, include_bias=False)  # 尝试更高的次数
+    poly = PolynomialFeatures(degree=2, include_bias=False)
     X_poly = poly.fit_transform(X_standardized)
     return X_poly
 
 def train_logreg(X, y):
     X_processed = preprocess_data(X)  # 预处理训练数据
+    model = RandomForestClassifier(n_estimators=113, max_depth=10, random_state=42)
+    model.fit(X_processed, y)
+    return model  # 返回训练好的模型
 
-    # 使用网格搜索调参
-    param_grid = {
-        'n_estimators': [100, 200, 300],
-        'max_depth': [5, 10, 15],
-        'class_weight': ['balanced', None]  # 处理不平衡数据
-    }
-
-    model = RandomForestClassifier(random_state=42)
-    grid_search = GridSearchCV(model, param_grid, cv=5)
-    grid_search.fit(X_processed, y)
-    return grid_search.best_estimator_  # 返回最佳模型
-
-def predict_logreg(model, X):
+def predict_logreg(X, model):
     # 确保输入数据是一个 numpy 数组
     if len(X.shape) == 1:
         X = X.reshape(1, -1)  # 保持输入数据的形状为 (1, n)，符合模型的输入要求