Harnessing GPT Assistants for Superior Model Ensembles: A Beginner’s Guide to AI Stacked Classifiers


OpenAI’s API allows users to programmatically create custom GPTs, referred to as Assistants, which can be instructed to write and execute code on provided data. This opens many exciting possibilities in data science, in particular the use of multiple Assistants to help build large scale, powerful machine learning ensemble methods that might otherwise be unfeasible.

Model stacking is an advanced machine learning technique where multiple base models, typically of different types, are trained on the same data and their predictions used as input for a final ""meta-model"". While it is a powerful technique, stacking is generally impractical for most data scientists due to its heavy resource requirements and time-consuming architecture. However, by creating multiple AI Assistants through the API, these types of multi-model ensembles can be easily and quickly created.

In this presentation, I will show how a single user with a beginner level knowledge of python can create a “swarm” of AI Assistants that train a series of models for use in a model-stacking ensemble classifier that outperforms traditional ML models on the same data. We will go over each step from getting set up with the API to orchestrating an AI swarm, to collecting their output for the final Meta model predictions.


Jason is a data scientist at Western Governor’s University who specializes in predictive modeling and forecasting. He received his PhD in molecular and cellular biology from the Oregon State University and has over 20 years of experience in academia and research at both online and traditional academic institutions. He is currently focused on using data science to better understand and improve student outcomes. Jason is passionate about merging Artificial Intelligence with data science and playing a part in the transformative impact AI will have on how data science is done.

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