LatestMachine LearningReinforcement Learning-Driven Adaptive Model Selection and Blending for Supervised Learning 0 like February 3, 2025Share this postAuthor(s): Shenggang Li Originally published on Towards AI. Inspired by Deepseeker: Dynamically Choosing and Combining ML Models for Optimal PerformanceThis member-only story is on us. Upgrade to access all of Medium.Photo by Agence Olloweb on UnsplashMachine learning model selection has always been a challenge. Whether youre predicting stock prices, diagnosing diseases, or optimizing marketing campaigns, the question remains: which model works best for my data? Traditionally, we rely on cross-validation to test multiple models XGBoost, LGBM, Random Forest, etc. and pick the best one based on validation performance. But what if different parts of the dataset require different models? Or what if blending multiple models dynamically could improve accuracy?This idea hit me while reading about Deepseeker R1, an advanced large language model (LLM) that adapts dynamically to improve performance. Inspired by its reinforcement learning (RL)-based optimization, I wondered: can we apply a similar RL-driven strategy to supervised learning? Instead of manually selecting a model, why not let reinforcement learning learn the best strategy for us?Imagine an RL agent acting like a data scientist analyzing dataset characteristics, testing different models, and learning which performs best. Even better, rather than just picking one model, it could blend models dynamically based on data patterns. For instance, in a financial dataset, XGBoost might handle structured trends well, while LGBM might Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post