Parameters of H2OGridSearch¶
Affected Class¶
ai.h2o.sparkling.ml.algos.H2OGridSearch
Parameters¶
Each parameter has also a corresponding getter and setter method. (E.g.:
label
->getLabel()
,setLabel(...)
)
- algo
Specifies the algorithm for grid search
Scala default value:
null
; Python default value:None
- hyperParameters
Hyper Parameters
Scala default value:
Map()
; Python default value:{}
- parallelism
Level of model-building parallelism, the possible values are:
0 -> H2O selects parallelism level based on cluster configuration, such as number of cores
1 -> Sequential model building, no parallelism
n>1 -> n models will be built in parallel if possible
Default value:
1
- selectBestModelBy
Select best model by specific metric.If this value is not specified that the first model os taken.
Default value:
"AUTO"
- maxModels
Maximum number of models to build (optional).
Default value:
0
- maxRuntimeSecs
Maximum time to spend building models (optional).
Default value:
0.0
- seed
Seed for random number generator; set to a value other than -1 for reproducibility.
Scala default value:
-1L
; Python default value:-1
- stoppingMetric
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression). Possible values are
"AUTO"
,"deviance"
,"logloss"
,"MSE"
,"RMSE"
,"MAE"
,"RMSLE"
,"AUC"
,"AUCPR"
,"lift_top_group"
,"misclassification"
,"mean_per_class_error"
,"anomaly_score"
,"AUUC"
,"ATE"
,"ATT"
,"ATC"
,"qini"
,"custom"
,"custom_increasing"
.Default value:
"AUTO"
- stoppingRounds
Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable).
Default value:
0
- stoppingTolerance
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).
Default value:
0.001
- strategy
Hyperparameter space search strategy. Possible values are
"Unknown"
,"Cartesian"
,"RandomDiscrete"
,"Sequential"
.Default value:
"Cartesian"