Summary of Divide, Conquer, Combine Bayesian Decision Tree Sampling, by Jodie A. Cochrane et al.
Divide, Conquer, Combine Bayesian Decision Tree Sampling
by Jodie A. Cochrane, Adrian Wills, Sarah J. Johnson
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Decision trees are widely used predictive models due to their flexibility and interpretability, but quantifying the uncertainty of these predictions remains a challenge. This paper addresses this issue by employing a Bayesian inference approach using Markov Chain Monte Carlo (MCMC) methods. The authors propose a new method, DCC-Tree, which associates each distinct tree structure with a unique set of decision parameters. Unlike existing MCMC approaches, DCC-Tree efficiently samples the joint space of tree structures and decision parameters. Experimental results show that DCC-Tree performs comparably to Hamiltonian Monte Carlo (HMC) based methods while improving on consistency and reducing per-proposal complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision trees are a type of predictive model that helps make predictions based on data. But, it’s hard to know how sure you can be about those predictions. This paper tries to figure out how to do this better by using a special kind of math called Bayesian inference. They use something called Markov Chain Monte Carlo (MCMC) methods to get more accurate results. The new approach they came up with is called DCC-Tree and it helps solve some problems that other methods had. |
Keywords
* Artificial intelligence * Bayesian inference
