xgboostlinearnode properties

Last updated: Jan 17, 2024

XGBoost Linear node icon XGBoost 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.