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 attribute from the Booster.
Get attribute from the Booster.
attr name
attr value
Get attributes stored in the Booster as a Map.
Get attributes stored in the Booster as a Map.
A map contain attribute pairs.
Get importance of each feature based on weight only (number of splits), with specified feature names.
Get importance of each feature based on weight only (number of splits), with specified feature names.
featureScoreMap key: feature name, value: feature importance score
Get importance of each feature based on weight only (number of splits)
Get importance of each feature based on weight only (number of splits)
featureScoreMap key: feature index, value: feature importance score
Dump model as Array of string with specified feature names.
Dump model as Array of string with specified feature names.
Names of features.
Dump model as Array of string
Dump model as Array of string
featureMap file
bool Controls whether the split statistics are output.
Get importance of each feature based on information gain or cover , with specified feature names.
Get importance of each feature based on information gain or cover , with specified feature names. Supported: ["gain, "cover", "total_gain", "total_cover"]
featureScoreMap key: feature name, value: feature importance score
Get importance of each feature based on information gain or cover Supported: ["gain, "cover", "total_gain", "total_cover"]
Get importance of each feature based on information gain or cover Supported: ["gain, "cover", "total_gain", "total_cover"]
featureScoreMap key: feature index, value: feature importance score
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
Output feature contributions toward predictions of given data
Output feature contributions toward predictions of given data
dmatrix storing the input
Limit number of trees in the prediction; defaults to 0 (use all trees).
The feature contributions and bias.
XGBoostError
native error
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 attribute to the Booster.
Set attribute to the Booster.
attr name
attr value
set attributes
set attributes
attributes key-value map
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
Booster for xgboost, this is a model API that support interactive build of a XGBoost Model
DEVELOPER WARNING: A Java Booster must not be shared by more than one Scala Booster