5,320 research outputs found
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
COMET is a single-pass MapReduce algorithm for learning on large-scale data.
It builds multiple random forest ensembles on distributed blocks of data and
merges them into a mega-ensemble. This approach is appropriate when learning
from massive-scale data that is too large to fit on a single machine. To get
the best accuracy, IVoting should be used instead of bagging to generate the
training subset for each decision tree in the random forest. Experiments with
two large datasets (5GB and 50GB compressed) show that COMET compares favorably
(in both accuracy and training time) to learning on a subsample of data using a
serial algorithm. Finally, we propose a new Gaussian approach for lazy ensemble
evaluation which dynamically decides how many ensemble members to evaluate per
data point; this can reduce evaluation cost by 100X or more
TreeGrad: Transferring Tree Ensembles to Neural Networks
Gradient Boosting Decision Tree (GBDT) are popular machine learning
algorithms with implementations such as LightGBM and in popular machine
learning toolkits like Scikit-Learn. Many implementations can only produce
trees in an offline manner and in a greedy manner. We explore ways to convert
existing GBDT implementations to known neural network architectures with
minimal performance loss in order to allow decision splits to be updated in an
online manner and provide extensions to allow splits points to be altered as a
neural architecture search problem. We provide learning bounds for our neural
network.Comment: Technical Report on Implementation of Deep Neural Decision Forests
Algorithm. To accompany implementation here:
https://github.com/chappers/TreeGrad. Update: Please cite as: Siu, C. (2019).
"Transferring Tree Ensembles to Neural Networks". International Conference on
Neural Information Processing. Springer, 2019. arXiv admin note: text overlap
with arXiv:1909.1179
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