xgboosttreenode properties
XGBoost Tree© is an advanced implementation of a gradient boosting algorithm with a tree model as the base model. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. XGBoost Tree is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoost Tree node in SPSS Modeler exposes the core features and commonly used parameters. The node is implemented in Python.
xgboosttreenode 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 the fields as required. 
target 
field  The target fields. 
inputs 
field  The input fields. 
tree_method

string  The tree method for model building. Possible values are auto ,
exact , or approx . Default is auto . 
num_boost_round 
integer  The num boost round value for model building. Specify a value between 1 and
1000 . Default is 10 . 
max_depth 
integer  The max depth for tree growth. Specify a value of 1 or higher. Default is
6 . 
min_child_weight 
Double  The min child weight for tree growth. Specify a value of 0 or higher.
Default is 1 . 
max_delta_step 
Double  The max delta step for tree growth. Specify a value of 0 or higher. Default
is 0 . 
objective_type 
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. 
early_stopping 
Boolean  Whether to use the early stopping function. Default is False . 
early_stopping_rounds 
integer  Validation error needs to decrease at least every early stopping round(s) to continue
training. Default is 10 . 
evaluation_data_ratio 
Double  Ration of input data used for validation errors. Default is 0.3 . 
random_seed

integer  The random number seed. Any number between 0 and 9999999 .
Default is 0 . 
sample_size 
Double  The sub sample for control overfitting. Specify a value between 0.1 and
1.0 . Default is 0.1 . 
eta 
Double  The eta for control overfitting. Specify a value between 0 and
1 . Default is 0.3 . 
gamma 
Double  The gamma for control overfitting. Specify any number 0 or greater. Default
is 6 . 
col_sample_ratio 
Double  The colsample by tree for control overfitting. Specify a value between 0.01
and 1 . Default is 1 . 
col_sample_level 
Double  The colsample by level for control overfitting. Specify a value between 0.01
and 1 . Default is 1 . 
lambda 
Double  The lambda for control overfitting. Specify any number 0 or greater. Default
is 1 . 
alpha 
Double  The alpha for control overfitting. Specify any number 0 or greater. Default
is 0 . 
scale_pos_weight 
Double  The scale pos weight for handling imbalanced datasets. Default is 1 . 
use_HPO 