hex

Class SupervisedModel.SupervisedParameters

java.lang.Object

water.Iced

hex.Model.Parameters

hex.SupervisedModel.SupervisedParameters

All Implemented Interfaces:

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

Enclosing class:

SupervisedModel<M extends Model<M,P,O>,P extends SupervisedModel.SupervisedParameters,O extends SupervisedModel.SupervisedOutput>

public abstract static class SupervisedModel.SupervisedParameters
extends Model.Parameters

Supervised Model Parameters includes a response column, and whether or not rebalancing classes is desirable. Also includes a bunch of cheap cached convenience fields.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
boolean	_balance_classes Should all classes be over/under-sampled to balance the class distribution?
float[]	_class_sampling_factors Desired over/under-sampling ratios per class (lexicographic order).
boolean	_convert_to_enum Convert the response column to an enum (forcing a classification instead of a regression) as needed.
float	_max_after_balance_size When classes are being balanced, limit the resulting dataset size to the specified multiple of the original dataset size.
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)
java.lang.String	_response_column Supervised models have an expected response they get to train with!

Fields inherited from class hex.Model.Parameters

_destination_key, _dropConsCols, _dropNA20Cols, _ignored_columns, _max_confusion_matrix_size, _score_each_iteration, _train, _valid

Constructor Summary

Constructors

Constructor and Description
SupervisedModel.SupervisedParameters()

Method Summary

Methods inherited from class hex.Model.Parameters

checksum_impl, defaultDropConsCols, defaultDropNA20Cols, missingColumnsType, read_lock_frames, read_unlock_frames, train, valid

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

_response_column

public java.lang.String _response_column

Supervised models have an expected response they get to train with!

_convert_to_enum

public boolean _convert_to_enum

Convert the response column to an enum (forcing a classification instead of a regression) as needed. The default is false, which means "do nothing" - accept the response column as-is and that alone drives the decision to do a classification vs regression.

_balance_classes

public boolean _balance_classes

Should all classes be over/under-sampled to balance the class distribution?

_max_after_balance_size

public float _max_after_balance_size

When classes are being balanced, limit the resulting dataset size to the specified multiple of the original dataset size. Maximum relative size of the training data after balancing class counts (can be less than 1.0)

_class_sampling_factors

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_hit_ratio_k

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)

Constructor Detail

SupervisedModel.SupervisedParameters

public SupervisedModel.SupervisedParameters()