机器学习41：利用KNN算法实现手写数字识别

摘要

        本文以手写数字识别任务，展示了使用K近邻（KNN）算法对灰度图像进行分类的完整流程。文章首先介绍了数据集构成（42000张28×28像素的手写数字图像，含784个特征及对应标签），并通过代码演示了数据读取、图像可视化及标签分布统计。随后，对特征进行归一化处理，采用分层采样划分训练集与测试集以避免标签分布失衡，使用KNN分类器进行模型训练与评估，最终准确率达到约96.57%。此外，文章还演示了如何保存训练好的模型并用于识别外部手写数字图片，为KNN算法在图像识别领域的应用提供了可复用的实践范例。

Abstract

        This article demonstrates the complete process of using the K-Nearest Neighbors (KNN) algorithm for grayscale image classification through the task of handwritten digit recognition. It first introduces the dataset composition (42,000 grayscale images of handwritten digits, each 28×28 pixels with 784 features and corresponding labels), and shows data loading, image visualization, and label distribution statistics through code examples. Subsequently, features are normalized, and stratified sampling is applied to split training and test sets to avoid label imbalance. A KNN classifier is used for model training and evaluation, achieving an accuracy of approximately 96.57%. Furthermore, the article demonstrates how to save the trained model and use it to recognize external handwritten digit images, providing a reusable practical exampl