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Add support for catboost into boost_tree() #117
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Sorry for wrong comments, I was thinking it's an issue in our repo. |
Hello!
We'll have this fixed. |
Just getting to this now.... I'd love to include this; I've heard very good things about perfomance. One general thought that I have is that the R interface isn't very R-like. Would it be possible to have a conventional S3 interface that looks something like: catboost.formula(formula, data, <model arguments>)
catboost.default(x, y, <model arguments>) The current interface is very flexible but the biggest frustration that I've heard about
We are trying to push developers to design separate functions/design for the user-interface and the computational module. A lot of that is encapsulated in these guidelines. In summary, we don't want people to be frustrated by your user interface.
(small edit) |
I should have also said that we'd be happy to help with the R interface. We're building some foundational code that could help make those user interfaces easier to create. |
@topepo We are now trying to add CatBoost to CRAN, there is some delay from moderators, but I think this should happen soon. Also we have much more functionality in our Python package - there are cross-validation, a set of utils, and more. We would love to add this all to R using interfaces that are clear for R users. I have created an issue on our gh: catboost/catboost#777 |
hello! We implemented lightgbm and catboost engines in this package curso-r/treesnip. |
Thank you for sharing, @Athospd! 🙌 We may open an issue on that new repo with some details you all may want to explore on engine-specific arguments. I'm going to close this issue, since catboost is still not on CRAN and supporting R modeling engines not on CRAN is outside the scope of tidymodels goals. |
LightGBM is getting close to being on CRAN. Don't know how close catboost is. |
This issue has been automatically locked. If you believe you have found a related problem, please file a new issue (with a reprex: https://reprex.tidyverse.org) and link to this issue. |
catboost
(@catboost) is growing in popularity and has an R package which easily supports GPUs out of the box. @annaveronika has some nice benchmarks comparing this toxgboost
.It requires a
data.frame
(willtbl
s work?) for the predictors (data
) and avector
for the response (label
), which can be both categorical and numeric. Though categorical predictors need to befactor
s according to this notebook. Hopefully this can be worked out nicely with bothrecipes
and theformula
.It also allows categorical predictors to be one-hot encoded or not, which should make @topepo happy.
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