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"