Dropout regularization reduces overfitting in Neural networks, especially deep belief networks (srivastava14a). It also has the opportunity to accelerate learning because individual learning iterations are on a reduced set of the model.

The gradient boosted tree (like those xgboost or gbm) is known for being an excellent ensemble learner, but one that suffers from over-fitting.

Is there an analog to dropout regression that is used in GBM learning?
Does "subsampling" or "stochastic gradient learning" relate to this - does it count as a version of dropout regression?

I think that the stochastic gradient learning is about "randomly disabling inputs" and not "randomly disabling individual learners".

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    $\begingroup$ For me neural net dropout is analogue to random variable subspace aka. mtry of random forests. In the RF model each node can only be split by a random fraction of features, hereby achieving more a regularized ensemble. I see that the [xgboost package][1] has a colsample_bytree option which can reduce the number of variables available for each tree. This could be seen as an analouge to dropout. [1]: cran.r-project.org/web/packages/xgboost/xgboost.pdf $\endgroup$ – Soren Havelund Welling Mar 9 '16 at 13:26
  • $\begingroup$ Does gradient boosting suffer from overfitting? With proper use of early stopping and learning rates, I have always been able to use it effectively. $\endgroup$ – Matthew Drury Sep 1 '16 at 14:56
  • $\begingroup$ @MatthewDrury It can still overfit (easier than Random Forests for example) though. $\endgroup$ – Firebug May 8 '18 at 16:44
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    $\begingroup$ @Firebug Huh. I wonder what I was thinking when I posted that... $\endgroup$ – Matthew Drury May 8 '18 at 16:55
  • $\begingroup$ @MatthewDrury Haha happens to us all xD $\endgroup$ – Firebug May 8 '18 at 17:15

Check out this paper: DART: Dropouts meet Multiple Additive Regression Trees (Arxiv PDF).

Their interpertation of dropout is this: instead of developing the next tree from the residual of all previous trees, develop the next tree from the residual of a sample of previous trees. The effect on the model is similar in that individual components are forced to be more self-sufficient. They observe some reasonably significant gains.

As Soren points out, colsample_bytree and colsample_bylevel are analogous to input-layer dropout.

DART is available in xgboost already by setting booster="dart"

  • $\begingroup$ my version says "gblinear" or "gbtree" and does not give "dart". Is there another library that I need for xgboost in r to do this? $\endgroup$ – EngrStudent Sep 1 '16 at 16:15
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    $\begingroup$ It got added very recently, so your version may be slightly out of date or they may not have pushed it to the stable channels. You can get the most up-to-date version by installing from their github repository. github.com/dmlc/xgboost. For R: github.com/dmlc/xgboost/tree/master/R-package $\endgroup$ – Dex Groves Sep 1 '16 at 16:17

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