Execute grid tuning for xgboost
XGBoostGridTuner(
ModelType = "classification",
TrainOnFull. = TrainOnFull,
DebugMode. = DebugMode.,
TreeMethod. = TreeMethod,
Trees. = Trees,
Depth. = max_depth,
LearningRate. = eta,
min_child_weight. = min_child_weight,
subsample. = subsample,
colsample_bytree. = colsample_bytree,
LossFunction = LossFunction,
EvalMetric = eval_metric,
grid_eval_metric. = grid_eval_metric,
CostMatrixWeights = CostMatrixWeights,
TargetColumnName. = TargetColumnName,
datatrain. = datatrain,
datavalidate. = datavalidate,
datatest. = datatest,
EvalSets. = EvalSets,
TestTarget. = TestTarget,
FinalTestTarget. = FinalTestTarget,
TargetLevels. = TargetLevels,
MaxRunsWithoutNewWinner = MaxRunsWithoutNewWinner,
MaxModelsInGrid = MaxModelsInGrid,
MaxRunMinutes = MaxRunMinutes,
BaselineComparison. = BaselineComparison,
SaveModelObjects = SaveModelObjects,
metadata_path = metadata_path,
model_path = model_path,
ModelID = ModelID,
Verbose. = Verbose,
NumLevels. = NumLevels
)"classification"
TrainOnFull
DebugMode
TreeMethod
Trees
max_depth
eta
min_child_weight
subsample
colsample_bytree
LossFunction
EvalMetric
MultiClass
CostMatrixWeights
TargetColumnName
datatrain
datavalidate
datatest
EvalSets
TestTarget
FinalTestTarget
TargetLevels
MaxRunsWithoutNewWinner
MaxModelsInGrid
MaxRunMinutes
BaselineComparison
SaveModelObjects
metadata_path
model_path
ModelID
Verbose
NumLevels
MetricPeriods
ClassWeights
Other Reinforcement Learning:
CatBoostGridTuner(),
RL_Initialize(),
RL_ML_Update(),
RL_Update(),
RPM_Binomial_Bandit()