public static class PSVMModel.PSVMParameters
extends hex.Model.Parameters
Modifier and Type | Field and Description |
---|---|
boolean |
_disable_training_metrics |
double |
_fact_threshold |
double |
_feasible_threshold |
double |
_gamma |
double |
_hyper_param |
hex.genmodel.algos.psvm.KernelType |
_kernel_type |
int |
_max_iterations |
double |
_mu_factor |
double |
_negative_weight |
double |
_positive_weight |
double |
_rank_ratio |
long |
_seed |
double |
_surrogate_gap_threshold |
double |
_sv_threshold |
double |
_zero_threshold |
_auc_type, _auto_rebalance, _auuc_nbins, _auuc_type, _balance_classes, _categorical_encoding, _check_constant_response, _checkpoint, _class_sampling_factors, _custom_distribution_func, _custom_metric_func, _cv_fold, _distribution, _export_checkpoints_dir, _fold_assignment, _fold_column, _gainslift_bins, _huber_alpha, _ignore_const_cols, _ignored_columns, _is_cv_model, _keep_cross_validation_fold_assignment, _keep_cross_validation_models, _keep_cross_validation_predictions, _keep_cross_validation_predictions_precision, _main_model_time_budget_factor, _max_after_balance_size, _max_categorical_levels, _max_confusion_matrix_size, _max_runtime_secs, _nfolds, _offset_column, _parallelize_cross_validation, _preprocessors, _pretrained_autoencoder, _quantile_alpha, _response_column, _score_each_iteration, _stopping_metric, _stopping_rounds, _stopping_tolerance, _train, _treatment_column, _tweedie_power, _valid, _weights_column, MAX_SUPPORTED_LEVELS
Constructor and Description |
---|
PSVMParameters() |
Modifier and Type | Method and Description |
---|---|
java.lang.String |
algoName() |
java.lang.String |
fullName() |
java.lang.String |
javaName() |
Kernel |
kernel() |
long |
progressUnits() |
checksum, checksum, defaultDropConsCols, defaultStoppingTolerance, getCategoricalEncoding, getDependentKeys, getDistributionFamily, getFoldColumn, getMaxCategoricalLevels, getNonPredictors, getOffsetColumn, getOrMakeRealSeed, getResponseColumn, getTreatmentColumn, getUsedColumns, getWeightsColumn, hasCheckpoint, hasCustomMetricFunc, missingColumnsType, read_lock_frames, read_unlock_frames, setDistributionFamily, setTrain, train, valid
public long _seed
public double _hyper_param
public double _positive_weight
public double _negative_weight
public double _sv_threshold
public double _zero_threshold
public boolean _disable_training_metrics
public hex.genmodel.algos.psvm.KernelType _kernel_type
public double _gamma
public double _rank_ratio
public double _fact_threshold
public int _max_iterations
public double _feasible_threshold
public double _surrogate_gap_threshold
public double _mu_factor
public java.lang.String algoName()
algoName
in class hex.Model.Parameters
public java.lang.String fullName()
fullName
in class hex.Model.Parameters
public java.lang.String javaName()
javaName
in class hex.Model.Parameters
public long progressUnits()
progressUnits
in class hex.Model.Parameters
public Kernel kernel()