water.api

Class SupervisedModelParametersSchema<P extends SupervisedModel.SupervisedParameters,S extends SupervisedModelParametersSchema<P,S>>

java.lang.Object

water.Iced

water.api.Schema<P,S>

water.api.ModelParametersSchema<P,S>

water.api.SupervisedModelParametersSchema<P,S>

All Implemented Interfaces:

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

public abstract class SupervisedModelParametersSchema<P extends SupervisedModel.SupervisedParameters,S extends SupervisedModelParametersSchema<P,S>>
extends ModelParametersSchema<P,S>

An instance of a SupervisedModelParameters schema contains the common SupervisedModel build parameters (e.g., response_column).

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class water.api.ModelParametersSchema

ModelParametersSchema.ValidationMessageBase<I extends ModelBuilder.ValidationMessage,S extends ModelParametersSchema.ValidationMessageBase<I,S>>, ModelParametersSchema.ValidationMessageV2

Nested classes/interfaces inherited from class water.api.Schema

Schema.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	do_classification
float	max_after_balance_size When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.
static java.lang.String[]	own_fields
FrameV2.ColSpecifierV2	response_column

Fields inherited from class water.api.ModelParametersSchema

destination_key, ignored_columns, score_each_iteration, training_frame, validation_frame

Fields inherited from class water.api.Schema

__meta, _impl_class, _version_pattern

Constructor Summary

Constructors

Constructor and Description
SupervisedModelParametersSchema()

Method Summary

Methods

Modifier and Type	Method and Description
S	fillFromImpl(P impl) Version and Schema-specific filling from the implementation object.
P	fillImpl(P impl) Fill an impl object and any children from this schema and its children.

Methods inherited from class water.api.ModelParametersSchema

append_field_arrays, fields, writeParametersJSON

Methods inherited from class water.api.Schema

acceptsFrame, createAndFillImpl, createImpl, extractVersion, fillFromParms, getImplClass, getImplClass, getSchemaVersion, markdown, markdown, markdown, markdown, newInstance, register, registerAllSchemasIfNecessary, schema, schema, schema, schema, schema, schemaClass, schemaClass, schemaClass, schemaClass, schemas

Methods inherited from class water.Iced

clone, frozenType, read_impl, read, readExternal, readJSON_impl, readJSON, toJsonString, write_impl, write, writeExternal, writeHTML_impl, writeHTML, writeJSON_impl, writeJSON

Methods inherited from class java.lang.Object

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

Field Detail

own_fields

public static java.lang.String[] own_fields

response_column

@API(help="Response column",
     is_member_of_frames={"training_frame","validation_frame"},
     is_mutually_exclusive_with="ignored_columns",
     direction=INOUT)
public FrameV2.ColSpecifierV2 response_column

do_classification

@API(help="Convert the response column to an enum (forcing a classification instead of a regression) if needed.",
     direction=INOUT)
public boolean do_classification

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.

Constructor Detail

SupervisedModelParametersSchema

public SupervisedModelParametersSchema()

Method Detail

fillFromImpl

public S fillFromImpl(P impl)

Description copied from class: Schema

Version and Schema-specific filling from the implementation object.

Overrides:

fillFromImpl in class ModelParametersSchema<P extends SupervisedModel.SupervisedParameters,S extends SupervisedModelParametersSchema<P,S>>

fillImpl

public P fillImpl(P impl)

Description copied from class: Schema

Fill an impl object and any children from this schema and its children.

Overrides:

fillImpl in class ModelParametersSchema<P extends SupervisedModel.SupervisedParameters,S extends SupervisedModelParametersSchema<P,S>>