51,366 research outputs found
Stochastic Gradient Trees
We present an algorithm for learning decision trees using stochastic gradient
information as the source of supervision. In contrast to previous approaches to
gradient-based tree learning, our method operates in the incremental learning
setting rather than the batch learning setting, and does not make use of soft
splits or require the construction of a new tree for every update. We
demonstrate how one can apply these decision trees to different problems by
changing only the loss function, using classification, regression, and
multi-instance learning as example applications. In the experimental
evaluation, our method performs similarly to standard incremental
classification trees, outperforms state of the art incremental regression
trees, and achieves comparable performance with batch multi-instance learning
methods.Comment: Accepted at ACML 201
Infinitesimal gradient boosting
We define infinitesimal gradient boosting as a limit of the popular
tree-based gradient boosting algorithm from machine learning. The limit is
considered in the vanishing-learning-rate asymptotic, that is when the learning
rate tends to zero and the number of gradient trees is rescaled accordingly.
For this purpose, we introduce a new class of randomized regression trees
bridging totally randomized trees and Extra Trees and using a softmax
distribution for binary splitting. Our main result is the convergence of the
associated stochastic algorithm and the characterization of the limiting
procedure as the unique solution of a nonlinear ordinary differential equation
in a infinite dimensional function space. Infinitesimal gradient boosting
defines a smooth path in the space of continuous functions along which the
training error decreases, the residuals remain centered and the total variation
is well controlled.Comment: 51 pages, 5 figure
Small area estimation of the homeless in Los Angeles: An application of cost-sensitive stochastic gradient boosting
In many metropolitan areas efforts are made to count the homeless to ensure
proper provision of social services. Some areas are very large, which makes
spatial sampling a viable alternative to an enumeration of the entire terrain.
Counts are observed in sampled regions but must be imputed in unvisited areas.
Along with the imputation process, the costs of underestimating and
overestimating may be different. For example, if precise estimation in areas
with large homeless c ounts is critical, then underestimation should be
penalized more than overestimation in the loss function. We analyze data from
the 2004--2005 Los Angeles County homeless study using an augmentation of
stochastic gradient boosting that can weight overestimates and underestimates
asymmetrically. We discuss our choice to utilize stochastic gradient boosting
over other function estimation procedures. In-sample fitted and out-of-sample
imputed values, as well as relationships between the response and predictors,
are analyzed for various cost functions. Practical usage and policy
implications of these results are discussed briefly.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS328 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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