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 . 