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Bayesian Decision Trees Inspired from Evolutionary Algorithms
Bayesian Decision Trees (DTs) are generally considered a more advanced and accurate model than a regular Decision Tree (DT) because they can handle complex and uncertain data.
Parallel Approaches to Accelerate Bayesian Decision Trees
None-the-less, we propose two methods for exploiting parallelism in the MCMC: in the first, we replace the MCMC with another numerical Bayesian approach, the Sequential Monte Carlo (SMC) sampler, which has the appealing property that it is an inherently parallel algorithm; in the second, we consider data partitioning.
Single MCMC Chain Parallelisation on Decision Trees
Decision trees are highly famous in machine learning and usually acquire state-of-the-art performance.
