Docs
...
xgboostlinearnode properties

xgboostlinearnode properties

Last updated: Feb 11, 2025
xgboostlinearnode properties

XGBoost Linear node iconXGBoost Linear© is an advanced implementation of a gradient boosting algorithm with a linear model as the base model. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. The XGBoost Linear node in SPSS Modeler is implemented in Python.

Table 1. xgboostlinearnode properties
xgboostlinearnode properties Data type Property description
custom_fields boolean This option tells the node to use field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify fields as required.
target field  
inputs field  
alpha Double The alpha linear booster parameter. Specify any number 0 or greater. Default is 0.
lambda Double The lambda linear booster parameter. Specify any number 0 or greater. Default is 1.
lambdaBias Double The lambda bias linear booster parameter. Specify any number. Default is 0.
num_boost_round integer The num boost round value for model building. Specify a value between 1 and 1000. Default is 10.
objectiveType string The objective type for the learning task. Possible values are reg:linear, reg:logistic, reg:gamma, reg:tweedie, count:poisson, rank:pairwise, binary:logistic, or multi. Note that for flag targets, only binary:logistic or multi can be used. If multi is used, the score result will show the multi:softmax and multi:softprob XGBoost objective types.
random_seed integer The random number seed. Any number between 0 and 9999999. Default is 0.
useHPO Boolean Specify true or false to enable or disable the HPO options. If set to true, Rbfopt will be applied to find out the "best" One-Class SVM model automatically, which reaches the target objective value defined by the user with the target_objval parameter.