hex.schemas

Class NaiveBayesV3.NaiveBayesParametersV3

java.lang.Object

water.Iced

water.api.Schema<I,S>

water.api.schemas3.SchemaV3<P,S>

water.api.schemas3.ModelParametersSchemaV3<NaiveBayesModel.NaiveBayesParameters,NaiveBayesV3.NaiveBayesParametersV3>

hex.schemas.NaiveBayesV3.NaiveBayesParametersV3

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, water.Freezable

Enclosing class:

NaiveBayesV3

public static final class NaiveBayesV3.NaiveBayesParametersV3
extends water.api.schemas3.ModelParametersSchemaV3<NaiveBayesModel.NaiveBayesParameters,NaiveBayesV3.NaiveBayesParametersV3>

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class water.api.schemas3.SchemaV3

water.api.schemas3.SchemaV3.Meta

Field Summary

Fields

Modifier and Type	Field and Description
boolean	balance_classes For imbalanced data, balance training data class counts via over/under-sampling.
float[]	class_sampling_factors Desired over/under-sampling ratios per class (lexicographic order).
boolean	compute_metrics
double	eps_prob
double	eps_sdev
static java.lang.String[]	fields
double	laplace
float	max_after_balance_size When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.
int	max_confusion_matrix_size For classification models, the maximum size (in terms of classes) of the confusion matrix for it to be printed.
int	max_hit_ratio_k The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
double	min_prob
double	min_sdev
long	seed

Fields inherited from class water.api.schemas3.ModelParametersSchemaV3

categorical_encoding, checkpoint, distribution, fold_assignment, fold_column, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_predictions, max_categorical_levels, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, quantile_alpha, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, tweedie_power, validation_frame, weights_column

Fields inherited from class water.api.schemas3.SchemaV3

__meta

Constructor Summary

Constructors

Constructor and Description
NaiveBayesV3.NaiveBayesParametersV3()

Method Summary

Methods inherited from class water.api.schemas3.ModelParametersSchemaV3

append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSON

Methods inherited from class water.api.schemas3.SchemaV3

writeJSON

Methods inherited from class water.api.Schema

createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall

Methods inherited from class water.Iced

asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonString, write, writeExternal

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

fields

public static java.lang.String[] fields

balance_classes

@API(help="Balance training data class counts via over/under-sampling (for imbalanced data).",
     level=secondary,
     direction=INOUT)
public boolean balance_classes

For imbalanced data, balance training data class counts via over/under-sampling. This can result in improved predictive accuracy.

class_sampling_factors

@API(help="Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.",
     level=expert,
     direction=INOUT)
public float[] class_sampling_factors

Desired over/under-sampling ratios per class (lexicographic order). Only when balance_classes is enabled. If not specified, they will be automatically computed to obtain class balance during training.

max_after_balance_size

@API(help="Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.",
     level=expert,
     direction=INOUT)
public float max_after_balance_size

When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.

max_confusion_matrix_size

@API(help="[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs",
     level=secondary,
     direction=INOUT)
public int max_confusion_matrix_size

For classification models, the maximum size (in terms of classes) of the confusion matrix for it to be printed. This option is meant to avoid printing extremely large confusion matrices.

max_hit_ratio_k

@API(help="Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)",
     level=secondary,
     direction=INOUT)
public int max_hit_ratio_k

The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

laplace

@API(help="Laplace smoothing parameter",
     gridable=true)
public double laplace

min_sdev

@API(help="Min. standard deviation to use for observations with not enough data",
     gridable=true)
public double min_sdev

eps_sdev

@API(help="Cutoff below which standard deviation is replaced with min_sdev",
     gridable=true)
public double eps_sdev

min_prob

@API(help="Min. probability to use for observations with not enough data",
     gridable=true)
public double min_prob

eps_prob

@API(help="Cutoff below which probability is replaced with min_prob",
     gridable=true)
public double eps_prob

compute_metrics

@API(help="Compute metrics on training data",
     gridable=true)
public boolean compute_metrics

seed

@API(help="Seed for pseudo random number generator (only used for cross-validation and fold_assignment=\"Random\" or \"AUTO\")",
     level=expert,
     direction=INOUT,
     gridable=true)
public long seed

Constructor Detail

NaiveBayesV3.NaiveBayesParametersV3

public NaiveBayesV3.NaiveBayesParametersV3()