hex.schemas

Class SharedTreeV3.SharedTreeParametersV3<P extends SharedTreeModel.SharedTreeParameters,S extends SharedTreeV3.SharedTreeParametersV3<P,S>>

java.lang.Object

water.Iced

water.api.Schema<P,S>

water.api.ModelParametersSchema<P,S>

hex.schemas.SharedTreeV3.SharedTreeParametersV3<P,S>

All Implemented Interfaces:

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

Direct Known Subclasses:

DRFV3.DRFParametersV3, GBMV3.GBMParametersV3

Enclosing class:

SharedTreeV3<B extends SharedTree,S extends SharedTreeV3<B,S,P>,P extends SharedTreeV3.SharedTreeParametersV3>

public static class SharedTreeV3.SharedTreeParametersV3<P extends SharedTreeModel.SharedTreeParameters,S extends SharedTreeV3.SharedTreeParametersV3<P,S>>
extends water.api.ModelParametersSchema<P,S>

See Also:

Serialized Form

Nested Class Summary

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

water.api.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.
boolean	build_tree_one_node
float[]	class_sampling_factors Desired over/under-sampling ratios per class (lexicographic order).
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_depth
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_rows
int	nbins
int	nbins_cats
int	ntrees
water.api.FrameV3.ColSpecifierV3	offset_column
static java.lang.String[]	own_fields
double	r2_stopping
water.api.FrameV3.ColSpecifierV3	response_column
long	seed
water.api.FrameV3.ColSpecifierV3	weights_column

Fields inherited from class water.api.ModelParametersSchema

ignore_const_cols, ignored_columns, model_id, score_each_iteration, training_frame, validation_frame

Fields inherited from class water.api.Schema

__meta, _impl_class, _version_pattern, EXCLUDE_FIELDS, INCLUDE_FIELDS

Constructor Summary

Constructors

Constructor and Description
SharedTreeV3.SharedTreeParametersV3()

Method Summary

Methods inherited from class water.api.ModelParametersSchema

append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSON

Methods inherited from class water.api.Schema

acceptsFrame, createAndFillImpl, createImpl, extractVersion, fillFromParms, getExperimentalVersion, getHighestSupportedVersion, getImplClass, getImplClass, getLatestVersion, 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 water.api.FrameV3.ColSpecifierV3 response_column

weights_column

@API(help="Column with observation weights",
     is_member_of_frames={"training_frame","validation_frame"},
     is_mutually_exclusive_with={"ignored_columns","response_column"},
     direction=INOUT)
public water.api.FrameV3.ColSpecifierV3 weights_column

offset_column

@API(help="Offset column",
     is_member_of_frames={"training_frame","validation_frame"},
     is_mutually_exclusive_with={"ignored_columns","response_column","weights_column"},
     direction=INOUT)
public water.api.FrameV3.ColSpecifierV3 offset_column

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="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)

ntrees

@API(help="Number of trees.",
     gridable=true)
public int ntrees

max_depth

@API(help="Maximum tree depth.",
     gridable=true)
public int max_depth

min_rows

@API(help="Fewest allowed (weighted) observations in a leaf (in R called \'nodesize\').",
     gridable=true)
public double min_rows

nbins

@API(help="For numerical columns (real/int), build a histogram of this many bins, then split at the best point",
     gridable=true)
public int nbins

nbins_cats

@API(help="For categorical columns (enum), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.",
     gridable=true)
public int nbins_cats

r2_stopping

@API(help="Stop making trees when the R^2 metric equals or exceeds this",
     level=secondary)
public double r2_stopping

seed

@API(help="Seed for pseudo random number generator (if applicable)")
public long seed

build_tree_one_node

@API(help="Run on one node only; no network overhead but fewer cpus used.  Suitable for small datasets.",
     level=secondary)
public boolean build_tree_one_node

Constructor Detail

SharedTreeV3.SharedTreeParametersV3

public SharedTreeV3.SharedTreeParametersV3()