写在前面：
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历史文章回顾：
灰狼优化算法：【智能优化算法】灰狼优化算法【附python实现代码】
白鲸优化算法：【智能优化算法】白鲸优化算法【附python实现代码】
【智能优化算法】粒子群优化KNN分类算法【附python实现代码】
【智能优化算法】粒子群优化随机森林分类算法【附python实现代码】
【智能优化算法】粒子群优化LightGBM分类算法【附python实现代码】
1、介绍
网格搜索是一种在多个维度上搜索最优解的方法，主要用于解决多变量问题，特别是寻找极值（包括极小值和极大值）。以下是网格搜索在不同领域的应用和定义：
- 化学领域：网格搜索被定义为在寻找多变量问题的所有极值中，以固定增量改变每个变量的搜索极值的方法。
信息技术领域：网格搜索通过建立跨越Web的信息分布和集成应用程序逻辑，利用现有的网络基础设施、协议规范、Web和数据库技术，为用户提供一体化的智能信息平台。这个平台的目标是创建一种基于Internet的新一代信息平台和软件基础设施，实现全面的信息资源共享。 - 机器学习和模式识别领域：网格搜索算法是一种数学方法，用于确定最优参数组合以实现最佳性能。这种方法的核心思想是通过枚举某个空间中的所有可能解，并以某种评价准则度量各种解，从而寻求最佳解。通常，可以使用函数的参数空间组成一个网格，并在每个网格点处测试样本，然后根据测试得出的性能结果进行比较，最终确定最有效的参数组合。
- 模型超参数优化：网格搜索也是一项模型超参数优化技术，常用于优化三个或更少数量的超参数。对于每个超参数，使用者选择一个较小的有限集去探索，然后这些超参数的笛卡尔乘积得到若干组超参数。网格搜索使用每组超参数训练模型，并挑选验证集误差最小的超参数作为最好的超参数。
总的来说，网格搜索是一种强大的工具，可以在多个维度上搜索最优解，适用于各种领域和问题。
【From 大模型】
2、实战代码
# -*- coding: utf-8 -*-
"""
Created on Fri May 3 21:55:32 2024@author: 63454https://zhuanlan.zhihu.com/p/647588686
"""from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import time# 加载数据集
wine = load_wine()
X = wine.data
y = wine.target # 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10) print("---------------------使用默认参数----------------------------")
# 初始化随机森林分类器
model = RandomForestClassifier(random_state=99)
# 训练
model.fit(X_train, y_train)
# 预测
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("默认参数 accuracy:", acc)print("---------------------参数寻优----------------------------")
t1 = time.time()
# 定义参数网格
param_grid = { 'n_estimators': [500, 600, 700, 800], # range(500, 1000)'max_depth': [None, 5, 10, 15, 20], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8], 'max_features': ['auto', 'sqrt', 'log2'], 'bootstrap': [True, False],
} # 初始化随机森林分类器
model = RandomForestClassifier(random_state=99)
# 初始化网格搜索对象
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy', verbose=2)
# 执行网格搜索
grid_search.fit(X_train, y_train)
t2 = time.time()
# 输出最优参数
print("Best parameters:")
print()
print(grid_search.best_params_)
print("time:", t2-t1)print("---------------------最优模型----------------------------")
model_best_params = grid_search.best_params_
model = grid_search.best_estimator_
# 训练
model.fit(X_train, y_train)
# 预测
y_pred = model.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("最优参数 accuracy:", acc)
终端输出：
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=600; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=700; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=2, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=700; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=700; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=800; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=3, min_samples_split=10, n_estimators=800; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=500; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=500; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=700; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=800; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=500; total time= 0.5s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=600; total time= 0.6s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=600; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=700; total time= 0.8s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time= 0.9s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time= 0.7s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=800; total time= 0.2s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=500; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=600; total time= 0.1s
[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=600; total time= 0.1s
3、总结
网格搜索（Grid Search）作为一种参数寻优技术，具有其独特的优点和缺点。
优点：
全面搜索：网格搜索是一种全面的搜索策略，能够穷尽所有参数的所有可能组合。这种策略可以确保在设定的参数范围内，找到最优的参数组合，从而达到最佳的效果，避免陷入局部最优解。
直观易懂：网格搜索的方法简单直接，易于理解和实现。它通过遍历所有可能的参数组合来找到最优的超参数集，对于初学者来说，是一种非常直观的超参数调优方法。
适用于参数量较少的情况：在所需设置的参数数目即参数维数较少的情况下，网格搜索算法的运算复杂度往往比较低，同时可以节省时间成本。此外，网格搜索算法可以并行计算，每组参数组合之间是相互独立没有相关联系的，因此可以在一定范围的区间内，从初始位置同时向多个方向出发搜索。
缺点：
计算成本高：网格搜索的主要缺点是计算成本非常高，尤其是当超参数空间很大或者模型训练时间很长时。因为需要尝试大量的参数组合，这会导致搜索时间过长，甚至在某些情况下变得不实际。
可能错过最优参数：网格搜索只能在有限的、预设的参数组合中进行搜索，因此可能会错过最优参数。如果预设的参数空间区域小，或者参数的取值范围设置不当，就有可能导致搜索不到最佳的参数值。
不适用于大规模数据集：对于中等或大规模数据量的搜索问题，网格搜索需要遍历所有参数的所有可能性，这会耗费过多的时间成本，搜索代价高昂。在大多数的设备中，对于几万个待寻优参数，每个参数有数千个候选值的情况，预计需要几天的时间来搜索最佳的参数组合。
因此，在选择是否使用网格搜索进行参数寻优时，需要根据实际问题的特点和需求进行权衡和选择。对于参数空间较小、计算资源充足的情况，网格搜索是一个不错的选择。然而，对于参数空间较大或计算资源有限的情况，可能需要考虑其他更为高效的参数寻优方法，如随机搜索（Random Search）等。
参考：
https://blog.csdn.net/qq_41076797/article/details/102755904
https://zhuanlan.zhihu.com/p/647588686
