Estimate Gradient Boosted Trees
To estimate a Gradient Boosted Trees model model select the type
(i.e., Classification or Regression), response variable, and one or more
explanatory variables. Press the Estimate button or
CTRL-enter (CMD-enter on mac) to generate
results.
The model can be “tuned” by changing by adjusting the parameter
inputs available in Radiant. In addition to these parameters, any others
can be adjusted in Report > Rmd. The best way to determine
the optimal values for all these hyper-parameters is to use
Cross-Validation. In Radiant, you can use the cv.gbt
function for this purpose. See the
documentation
for more information.
For more information on parameters that can be set for XGBoost, see the links below
Add code to
Report
> Rmd to (re)create the analysis by clicking the
icon on the bottom
left of your screen or by pressing ALT-enter on your
keyboard.
For an overview of related R-functions used by Radiant to estimate a neural network model see Model > Neural network.
The key function from the xgboost package used in the
gbt tool is xgboost.