update with give grad and hess
update with give grad and hess
training data
first order of gradient
seconde order of gradient
Dispose the booster when it is no longer needed
evaluate with given customized Evaluation class
evaluate with given customized Evaluation class
evaluation matrix
evaluation names
custom evaluator
eval information
evaluate with given dmatrixs.
evaluate with given dmatrixs.
dmatrixs for evaluation
name for eval dmatrixs, used for check results
current eval iteration
eval information
Get importance of each feature
Get importance of each feature
featureMap key: feature index, value: feature importance score
Dump model as Array of string
Dump model as Array of string
featureMap file
bool Controls whether the split statistics are output.
Predict with data
Predict with data
dmatrix storing the input
Whether to output the raw untransformed margin value.
Limit number of trees in the prediction; defaults to 0 (use all trees).
predict result
Predict the leaf indices
Predict the leaf indices
dmatrix storing the input
Limit number of trees in the prediction; defaults to 0 (use all trees).
predict result
XGBoostError native error
save model to Output stream
save model to Output stream
Output stream
save model to modelPath
save model to modelPath
model path
Set parameter to the Booster.
Set parameter to the Booster.
param name
param value
set parameters
set parameters
parameters key-value map
update with customize obj func
update with customize obj func
training data
customized objective class
Update (one iteration)
Update (one iteration)
training data
current iteration number