marlesson/meta-bandit-selector
The Contextual Meta-Bandit (CMB) can be used to select models using the context with online learning based on Reiforcement Learning problem. It's can be used for recommender system ensemble, A/B test, and other dynamic model selector problem.
This helps you dynamically choose the best model for a given situation, like selecting which recommendation algorithm to use for a specific user. It takes in contextual information about the current situation and the outputs from multiple available models, then tells you which model's output to use. This is for data scientists, machine learning engineers, and product managers who need to optimize real-time decisions based on continuously evolving data.
No commits in the last 6 months.
Use this if you need to automatically select the optimal algorithm or model from a set of options in real-time, based on varying conditions and user feedback.
Not ideal if your decision-making process is static, requires human oversight for every choice, or doesn't involve multiple models competing for selection.
Stars
9
Forks
3
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 06, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/marlesson/meta-bandit-selector"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
WilliamLwj/PyXAB
PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms
jekyllstein/Reinforcement-Learning-Sutton-Barto-Exercise-Solutions
Chapter notes and exercise solutions for Reinforcement Learning: An Introduction by Sutton and Barto
cfoh/Multi-Armed-Bandit-Example
Learning Multi-Armed Bandits by Examples. Currently covering MAB, UCB, Boltzmann Exploration,...
matteocasolari/reinforcement-learning-an-introduction-solutions
Implementations for solutions to programming exercises of Reinforcement Learning: An...
BY571/Upside-Down-Reinforcement-Learning
Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published...