Comments (4)
@ldesreumaux Thank you for raising the issue!
I am not able to reproduce the result that you are showing.
With a fresh install from the developer version together with your code
devtools::install_github("Blunde1/agtboost/R-package")
library(agtboost)
data(caravan.train, package = "agtboost")
train <- caravan.train
gbt.train(train$y, train$x, loss_function="logloss", verbose=10, nrounds=500, learning_rate=0.1)
I got the following output
it: 1 | n-leaves: 2 | tr loss: 0.2143 | gen loss: 0.2145
it: 10 | n-leaves: 2 | tr loss: 0.1969 | gen loss: 0.201
it: 20 | n-leaves: 2 | tr loss: 0.1913 | gen loss: 0.1981
it: 30 | n-leaves: 2 | tr loss: 0.1887 | gen loss: 0.1977
C++ object <000001e8ce835610> of class 'ENSEMBLE' <000001e8cdeae5a0>
I obtained almost identical results at consecutive runs.
May I ask which system/compiler you are using, or if you have worked on the internal C++ code?
The tree-depths you obtain are vastly different from mine, so something strange is deffinitely going on here.
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Indeed, the issue cannot be reproduced with the development version. I can only reproduce it when agtboost is installed with:
install.packages("agtboost")
And it does not seem to be platform-dependent: I reproduced it on Windows 10 and openSUSE 12.3 with R 4.0.5.
from agtboost.
Thank you, I was now able to reproduce it on R 4.0.5 (2021-03-31). Strangely CRAN and development version should be the same. I will look into it.
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Hoping this will be fixed in v0.9.2.
Waiting for CRAN mirrors to be updated to test with docker
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Related Issues (20)
- Handle categorical features internally
- Add `offset` possibility to `gbt.train` and `predict`
- Let `agtboost` handle random effects / latent variables
- Let users define custom loss-functions
- Crate viginette or updated documentation / illustration of theory
- Automatic L2-regularization
- Automatic stochastic (down)sampling in `agtboost`
- Influence-adjusted derivatives-calculations
- Fast histogram algorithm
- Monotone constraints
- Low-frequent count-data produces root-trees and does not converge
- agtboost tests
- Optimal initial prediction
- Use github-actions instead of Travis
- Stripping debugging information does not flow with CRAN
- Infinite boosting rounds with "logloss" loss function HOT 4
- demo poison-regression.R missing package name
- Memory leaks in gbt.load() HOT 18
- Make agtboost model object entirely numerical
- Refactor serialization to be more efficient
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