Beyond A/B Testing: How Contextual Bandits Revolutionize Experimentation in Machine Learning

Author(s): Joseph Robinson, Ph.D.

Originally published on Towards AI.

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In Part 1, we introduced the concept of Contextual Bandits, a powerful technique for solving real-time decision-making problems. In Part 2, we compared Contextual Bandits to Supervised Learning, highlighting the advantages of adaptive optimization over static learning.

Now, we focus on an age-old question in experimentation: A/B Testing or Contextual Bandits?

A/B Testing has been the gold standard for experimentation for years, offering simplicity and clear insights. However, it has limitations, particularly in dynamic environments where user behavior evolves and opportunity costs compound during fixed testing periods. Enter Contextual Bandits, a method that balances exploration (i.e., trying new options) with exploitation (i.e., leveraging what works) to optimize real-time decisions and personalize experiences.

This blog will explain when and why to choose each approach using a clear 5-factor decision framework. You’ll have a practical toolkit to align experimentation methods with your business goals, data constraints, and operational complexity by the end.

Is it time to keep it simple, or should you adapt dynamically?

Let’s find out. 🚀

· A/B Testing vs. Contextual Bandits: A Quick Recap ∘ A/B Testing: Static Allocation and Global Decisions ∘ Contextual Bandits: Adaptive… Read the full blog for free on Medium.

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Published via Towards AI