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Last updated: Feb 11, 2025
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.
properties |
Data type | Property description |
---|---|---|
|
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. |
|
field | |
|
field | |
|
Double | The alpha linear booster parameter. Specify any number or greater. Default
is . |
|
Double | The lambda linear booster parameter. Specify any number or greater.
Default is . |
|
Double | The lambda bias linear booster parameter. Specify any number. Default is
. |
|
integer | The num boost round value for model building. Specify a value between and
. Default is . |
|
string | The objective type for the learning task. Possible values are ,
, , ,
, , ,
or . Note that for flag targets, only or
can be used. If is used, the score result will show
the and XGBoost objective
types. |
|
integer | The random number seed. Any number between and .
Default is . |
|
Boolean | Specify or to enable or disable the HPO options.
If set to , 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
parameter. |
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