kapelner
commented
1 year ago Can't get to it. Too busy.
On Thu, Mar 2, 2023 at 7:03 PM schwa021 @.***> wrote:
I was wondering if you were still planning on implementing multi-class models. The following quote is from Kapelner A, Bleich J. bartMachine: Machine Learning with Bayesian Additive Regression Trees. J. Stat. Soft. [Internet]. 2016 Apr. 4;70(4):1-40.
" 2.3. BART for classification BART can easily be modified to handle classification problems for categorical response variables. In Chipman et al. (2010), only binary outcomes were explored but recent work has extended BART to the multiclass problem (Kindo, Wang, and Pe 2013). Our implementation handles binary classification and we plan to implement multiclass outcomes in a future release."
(emphasis mine)
I currently have to switch to an alternative package, with substantially different formatting requirements, output, etc... It would be wonderful to have that capacity in bartMachine.
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-- Adam Kapelner, Ph.D. Associate Professor of Mathematics Director of the Undergraduate Data Science and Statistics Program Queens College, City University of New York 65-30 Kissena Blvd., Kiely Hall Room 604 Flushing, NY, 11367 M: 516-435-6795 kapelner.com (CV https://kapelner.com/assets/kapelner/adk_cv-b7620260cf3f56d1d1f138327ba50f9b7f5147b5fe5084e9f03fa5228cec33e8.pdf | scholar https://scholar.google.com/citations?user=TzgMmnoAAAAJ | github https://github.com/kapelner | R packages https://scholar.rpkg.net/aut/Adam+Kapelner | linkedin https://www.linkedin.com/in/adam-kapelner/)
This is an enhancement request - not an Issue.
I was wondering if you were still planning on implementing multi-class models. The following quote is from Kapelner A, Bleich J. bartMachine: Machine Learning with Bayesian Additive Regression Trees. J. Stat. Soft. [Internet]. 2016 Apr. 4;70(4):1-40.
"2.3. BART for classification BART can easily be modified to handle classification problems for categorical response variables. In Chipman et al. (2010), only binary outcomes were explored but recent work has extended BART to the multiclass problem (Kindo, Wang, and Pe 2013). Our implementation handles binary classification and we plan to implement multiclass outcomes in a future release."
(emphasis mine)
I currently have to switch to an alternative package, with substantially different formatting requirements, output, etc... It would be wonderful to have that capacity in
bartMachine.