cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
  • Instantly extract effect sizes, F-ratios, and FDR-adjusted p-values from your models with the Calculate Effects Sizes extension, available now in the JMP Marketplace!
  • New to JMP? Join us Sept. 23-24 for the Early User Edition of Discovery Summit, tailor-made for new users. Register now for free!
  • See how to use the JMP Marketplace – Free tools to expand JMP capabilities. Register. July 10, 2 pm US Eastern Time.

Discussions

Solve problems, and share tips and tricks with other JMP users.
Choose Language Hide Translation Bar
uejimurashu
Level I

Is boosted tree in JMP the same as gradient boosted tree?

Is boosted tree in JMP the same as gradient boosted tree?
In many academic papers, the term gradient boosted tree is used, and I could not find the word boosted tree.

1 ACCEPTED SOLUTION

Accepted Solutions
SDF1
Super User

Re: Is boosted tree in JMP the same as gradient boosted tree?

Hi @uejimurashu ,

 

  Welcome to the JMP Community pages. In short, the answer is no, they are not the same. JMP has several different tree-based platforms that all have slightly different algorithms and implementations to how the modeling is optimized, as well as the hyperparameters for the model. These include partitioning, boosted trees, bootstrap forest (similar to random forest), and extreme gradient boosted trees (XGBoost). The last one is an additional add-in, that I believe you need at least JMP Pro v16.0 or higher to run -- it could be v15.0.

 

  You can find out more in JMP Help about boosted trees here, bootstrap forests here, and partitioning here. There's a good video on the different platforms here, and if you have Pro, then you can read up on XGBoost here.

 

Hope this helps!,

DS

View solution in original post

1 REPLY 1
SDF1
Super User

Re: Is boosted tree in JMP the same as gradient boosted tree?

Hi @uejimurashu ,

 

  Welcome to the JMP Community pages. In short, the answer is no, they are not the same. JMP has several different tree-based platforms that all have slightly different algorithms and implementations to how the modeling is optimized, as well as the hyperparameters for the model. These include partitioning, boosted trees, bootstrap forest (similar to random forest), and extreme gradient boosted trees (XGBoost). The last one is an additional add-in, that I believe you need at least JMP Pro v16.0 or higher to run -- it could be v15.0.

 

  You can find out more in JMP Help about boosted trees here, bootstrap forests here, and partitioning here. There's a good video on the different platforms here, and if you have Pro, then you can read up on XGBoost here.

 

Hope this helps!,

DS

Recommended Articles