机器学习40：超参数选择方法

摘要

        本文介绍了机器学习中两种重要的超参数优化技术：交叉验证与网格搜索。交叉验证通过将数据集划分为多个互斥子集，轮流作为验证集进行多次训练与评估，以获得更稳定的模型性能估计。网格搜索则通过穷举预设的超参数组合，系统性地寻找最优参数配置。文章进一步阐述了将两者结合使用的优势，并通过鸢尾花数据集上的KNN分类案例，演示了GridSearchCV API的具体用法，包括最优参数输出与模型评估。这些方法为提升模型泛化能力和调参效率提供了可靠工具。

Abstract

        This article introduces two important hyperparameter optimization techniques in machine learning: cross-validation and grid search. Cross-validation provides more stable model performance estimates by partitioning the dataset into multiple mutually exclusive subsets and using each in turn as a validation set for training and evaluation. Grid search systematically finds optimal parameter configurations by exhaustively searching over a predefined set of hyperparameter combinations. The article further explains the advantages of combining these two methods and demonstrates the use of the GridSearchCV API through a KNN classification case study on the Iris dataset, including optimal parameter output and model evaluation. These methods provide reliable tools for improving model generalization and tuning efficiency.

一．交叉验证（Cross-Validation）

1.交叉验证原理