xgboostasnode properties
XGBoost is an advanced implementation of a gradient boosting algorithm. Boosting algorithms iteratively learn weak classifiers and then add them to a final strong classifier. XGBoost is very flexible and provides many parameters that can be overwhelming to most users, so the XGBoostAS node in SPSS Modeler exposes the core features and commonly used parameters. The XGBoostAS node is implemented in Spark.
xgboostasnode properties 
Data type  Property description 

target_field 
field  List of the field names for target. 
input_fields 
field  List of the field names for inputs. 
nWorkers

integer  The number of workers used to train the XGBoost model. Default is 1 . 
numThreadPerTask

integer  The number of threads used per worker. Default is 1 . 
useExternalMemory

Boolean  Whether to use external memory as cache. Default is false. 
boosterType

string  The booster type to use. Available options are gbtree ,
gblinear , or dart . Default is gbtree . 
numBoostRound

integer  The number of rounds for boosting. Specify a value of 0 or higher. Default
is 10 . 
scalePosWeight

Double  Control the balance of positive and negative weights. Default is 1 . 
randomseed

integer  The seed used by the random number generator. Default is 0. 
objectiveType

string  The learning objective. Possible values are reg:linear ,
reg:logistic , reg:gamma , reg:tweedie ,
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. Default is
reg:linear . 
evalMetric

string  Evaluation metrics for validation data. A default metric will be assigned according to the
objective. Possible values are rmse , mae ,
logloss , error , merror ,
mlogloss , auc , ndcg , map , or
gammadeviance . Default is rmse . 
lambda 
Double  L2 regularization term on weights. Increasing this value will make the model more
conservative. Specify any number 0 or greater. Default is
1 . 
alpha 
Double  L1 regularization term on weights. Increasing this value will make the model more
conservative. Specify any number 0 or greater. Default is
0 . 
lambdaBias 
Double  L2 regularization term on bias. If the gblinear booster type is used, this
lambda bias linear booster parameter is available. Specify any number 0 or greater.
Default is 0 . 
treeMethod 
string  If the gbtree or dart booster type is used, this tree
method parameter for tree growth (and the other tree parameters that follow) is available. It
specifies the XGBoost tree construction algorithm to use. Available options are
auto , exact , or approx . Default is
auto . 
maxDepth 
integer  The maximum depth for trees. Specify a value of 2 or higher. Default is
6 . 
minChildWeight 
Double  The minimum sum of instance weight (hessian) needed in a child. Specify a value of
0 or higher. Default is 1 . 
maxDeltaStep 
Double  The maximum delta step to allow for each tree's weight estimation. Specify a value of
0 or higher. Default is 0 . 
sampleSize 
Double  The sub sample for is the ratio of the training instance. Specify a value between
0.1 and 1.0 . Default is 1.0 . 
eta 
Double  The step size shrinkage used during the update step to prevent overfitting. Specify a value
between 0 and 1 . Default is 0.3 . 
gamma 
Double  The minimum loss reduction required to make a further partition on a leaf node of the tree.
Specify any number 0 or greater. Default is 6 . 
colsSampleRatio 
Double  The sub sample ratio of columns when constructing each tree. Specify a value between
0.01 and 1 . Default is1 . 
colsSampleLevel 
Double  The sub sample ratio of columns for each split, in each level. Specify a value between
0.01 and 1 . Default is 1 . 
normalizeType 
string  If the dart booster type is used, this dart parameter and the following three dart parameters
are available. This parameter sets the normalization algorithm. Specify tree or
forest . Default is tree . 
sampleType 
string  The sampling algorithm type. Specify uniform or weighted .
Default is uniform . 
rateDrop 
Double  The dropout rate dart booster parameter. Specify a value between 0.0 and
1.0 . Default is 0.0 . 
skipDrop 
Double  The dart booster parameter for the probability of skip dropout. Specify a value between
0.0 and 1.0 . Default is 0.0 . 