Research on the Architecture and Implementation Path of Machine Learning Automation Experiment Platform from the Perspective of Artificial Intelligence

This article focuses on the architecture and implementation path of a machine learning automation experimental platform from the perspective of artificial intelligence. First, provide a detailed introduction to the principles of basic machine learning classifiers such as support vector machines, K-nearest neighbors, decision trees, and naive Bayes. Then, design an experimental platform based on MATLAB. The platform interface is divided into drawing and operation areas, with functions such as data loading, adjustment, saving, and model parameter setting. The classification model can be run by calling MATLAB's built-in functions. Users can use this platform to study the characteristics of classifiers and their performance on different datasets, providing a quick experimental environment for beginners in machine learning and promoting the learning and application of machine learning knowledge.