arXiv:2406.05260v1 Announce Sort: new
Summary: A typical goal within the evaluation of tabular information is estimating the conditional distribution (in distinction to solely producing predictions) of a set of “end result” variables given a set of “covariates”, which is usually known as the “density regression” downside. Past estimation on the conditional distribution, the generative potential of drawing artificial samples from the realized conditional distribution can be desired because it additional widens the vary of functions. We suggest a flow-based generative mannequin tailor-made for the density regression job on tabular information. Our move applies a sequence of tree-based piecewise-linear transforms on preliminary uniform noise to finally generate samples from complicated conditional densities of (univariate or multivariate) outcomes given the covariates and permits environment friendly analytical analysis of the fitted conditional density on any level within the pattern house. We introduce a coaching algorithm for becoming the tree-based transforms utilizing a divide-and-conquer technique that transforms most chance coaching of the tree-flow into coaching a set of binary classifiers–one at every tree split–under cross-entropy loss. We assess the efficiency of our technique beneath out-of-sample chance analysis and examine it with quite a lot of state-of-the-art conditional density learners on a spread of simulated and actual benchmark tabular datasets. Our technique persistently achieves comparable or superior efficiency at a fraction of the coaching and sampling finances. Lastly, we exhibit the utility of our technique’s generative potential by an utility to producing artificial longitudinal microbiome compositional information based mostly on coaching our move on a publicly obtainable microbiome research.
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