hex.schemas

Class GLMV3.GLMParametersV3

java.lang.Object

water.Iced

water.api.Schema<P,S>

water.api.ModelParametersSchema<P,S>

water.api.SupervisedModelParametersSchema<GLMModel.GLMParameters,GLMV3.GLMParametersV3>

hex.schemas.GLMV3.GLMParametersV3

All Implemented Interfaces:

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

Enclosing class:

GLMV3

public static final class GLMV3.GLMParametersV3
extends water.api.SupervisedModelParametersSchema<GLMModel.GLMParameters,GLMV3.GLMParametersV3>

See Also:

Serialized Form

Nested Class Summary

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

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

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

water.api.Schema.Meta

Field Summary

Fields

Modifier and Type	Field and Description
double[]	alpha
water.api.KeyV3.FrameKeyV3	beta_constraints
double	beta_epsilon
GLMModel.GLMParameters.Family	family
boolean	intercept
double[]	lambda
double	lambda_min_ratio
boolean	lambda_search
GLMModel.GLMParameters.Link	link
int	max_active_predictors
int	max_iterations
int	nlambdas
static java.lang.String[]	own_fields
double	prior
GLMModel.GLMParameters.Solver	solver
boolean	standardize
boolean	use_all_factor_levels

Fields inherited from class water.api.SupervisedModelParametersSchema

balance_classes, class_sampling_factors, max_after_balance_size, max_confusion_matrix_size, max_hit_ratio_k, response_column

Fields inherited from class water.api.ModelParametersSchema

drop_na20_cols, ignored_columns, model_id, 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
GLMV3.GLMParametersV3()

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

family

@API(help="Family. Use binomial for classification with logistic regression, others are for regression problems.",
     values={"gaussian","binomial","poisson","gamma"},
     level=critical)
public GLMModel.GLMParameters.Family family

solver

@API(help="Auto will pick solver better suited for the given dataset, in case of lambda search solvers may be changed during computation. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns.",
     values={"AUTO","IRLSM","L_BFGS"},
     level=critical)
public GLMModel.GLMParameters.Solver solver

alpha

@API(help="distribution of regularization between L1 and L2.",
     level=critical)
public double[] alpha

lambda

@API(help="regularization strength",
     required=false,
     level=critical)
public double[] lambda

lambda_search

@API(help="use lambda search starting at lambda max, given lambda is then interpreted as lambda min",
     level=critical)
public boolean lambda_search

nlambdas

@API(help="number of lambdas to be used in a search",
     level=critical)
public int nlambdas

standardize

@API(help="Standardize numeric columns to have zero mean and unit variance",
     level=critical)
public boolean standardize

max_iterations

@API(help="Maximum number of iterations",
     level=secondary)
public int max_iterations

beta_epsilon

@API(help="beta esilon -> consider being converged if L1 norm of the current beta change is below this threshold",
     level=secondary)
public double beta_epsilon

link

@API(help="",
     level=secondary,
     values={"family_default","identity","logit","log","inverse","tweedie"})
public GLMModel.GLMParameters.Link link

intercept

@API(help="include constant term in the model",
     level=expert)
public boolean intercept

prior

@API(help="prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality.",
     level=expert)
public double prior

lambda_min_ratio

@API(help="min lambda used in lambda search, specified as a ratio of lambda_max",
     level=expert)
public double lambda_min_ratio

use_all_factor_levels

@API(help="By default, first factor level is skipped from the possible set of predictors. Set this flag if you want use all of the levels. Needs sufficient regularization to solve!",
     level=secondary)
public boolean use_all_factor_levels

beta_constraints

@API(help="beta constraints",
     direction=INPUT)
public water.api.KeyV3.FrameKeyV3 beta_constraints

max_active_predictors

@API(help="Maximum number of active predictors during computation. Use as a stopping criterium to prevent expensive model building with many predictors.",
     direction=INPUT,
     level=expert)
public int max_active_predictors

Constructor Detail

GLMV3.GLMParametersV3

public GLMV3.GLMParametersV3()