water.bindings.pojos

Class ANOVAGLMParametersV3

java.lang.Object

water.bindings.pojos.SchemaV3

water.bindings.pojos.ModelParametersSchemaV3

water.bindings.pojos.ANOVAGLMParametersV3

public class ANOVAGLMParametersV3
extends ModelParametersSchemaV3

Field Summary

Fields

Modifier and Type	Field and Description
double[]	alpha Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties.
boolean	balanceClasses Balance training data class counts via over/under-sampling (for imbalanced data).
float[]	classSamplingFactors Desired over/under-sampling ratios per class (in lexicographic order).
boolean	computePValues Request p-values computation, p-values work only with IRLSM solver and no regularization
boolean	earlyStopping Stop early when there is no more relative improvement on train or validation (if provided).
GLMFamily	family Family.
int	highestInteractionTerm Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on...
double[]	lambda Regularization strength
boolean	lambdaSearch Use lambda search starting at lambda max, given lambda is then interpreted as lambda min
GLMLink	link Link function.
float	maxAfterBalanceSize Maximum relative size of the training data after balancing class counts (can be less than 1.0).
int	maxIterations Maximum number of iterations
GLMMissingValuesHandling	missingValuesHandling Handling of missing values.
boolean	nonNegative Restrict coefficients (not intercept) to be non-negative
int	nparallelism Number of models to build in parallel.
FrameKeyV3	plugValues Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)
double	prior Prior probability for y==1.
boolean	saveTransformedFramekeys true to save the keys of transformed predictors and interaction column.
long	seed Seed for pseudo random number generator (if applicable)
GLMSolver	solver AUTO will set the solver based on given data and the other parameters.
boolean	standardize Standardize numeric columns to have zero mean and unit variance
double	theta Theta
double	tweedieLinkPower Tweedie link power
double	tweedieVariancePower Tweedie variance power
int	type Refer to the SS type 1, 2, 3, or 4.

Fields inherited from class water.bindings.pojos.ModelParametersSchemaV3

aucType, categoricalEncoding, checkpoint, customDistributionFunc, customMetricFunc, distribution, exportCheckpointsDir, foldAssignment, foldColumn, gainsliftBins, huberAlpha, ignoreConstCols, ignoredColumns, keepCrossValidationFoldAssignment, keepCrossValidationModels, keepCrossValidationPredictions, maxCategoricalLevels, maxRuntimeSecs, modelId, nfolds, offsetColumn, parallelizeCrossValidation, quantileAlpha, responseColumn, scoreEachIteration, stoppingMetric, stoppingRounds, stoppingTolerance, trainingFrame, tweediePower, validationFrame, weightsColumn

Constructor Summary

Constructors

Constructor and Description
ANOVAGLMParametersV3() Public constructor

Method Summary

Modifier and Type	Method and Description
java.lang.String	toString() Return the contents of this object as a JSON String.

Methods inherited from class java.lang.Object

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

Field Detail

seed

public long seed

Seed for pseudo random number generator (if applicable)

standardize

public boolean standardize

Standardize numeric columns to have zero mean and unit variance

family

public GLMFamily family

Family. Use binomial for classification with logistic regression, others are for regression problems.

tweedieVariancePower

@SerializedName(value="tweedie_variance_power")
public double tweedieVariancePower

Tweedie variance power

tweedieLinkPower

@SerializedName(value="tweedie_link_power")
public double tweedieLinkPower

Tweedie link power

theta

public double theta

Theta

alpha

public double[] alpha

Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = 'L-BFGS'; 0.5 otherwise.

lambda

public double[] lambda

Regularization strength

lambdaSearch

@SerializedName(value="lambda_search")
public boolean lambdaSearch

Use lambda search starting at lambda max, given lambda is then interpreted as lambda min

solver

public GLMSolver solver

AUTO will set the solver based on given data and the other parameters. 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.

missingValuesHandling

@SerializedName(value="missing_values_handling")
public GLMMissingValuesHandling missingValuesHandling

Handling of missing values. Either MeanImputation, Skip or PlugValues.

plugValues

@SerializedName(value="plug_values")
public FrameKeyV3 plugValues

Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues)

nonNegative

@SerializedName(value="non_negative")
public boolean nonNegative

Restrict coefficients (not intercept) to be non-negative

computePValues

@SerializedName(value="compute_p_values")
public boolean computePValues

Request p-values computation, p-values work only with IRLSM solver and no regularization

maxIterations

@SerializedName(value="max_iterations")
public int maxIterations

Maximum number of iterations

link

public GLMLink link

Link function.

prior

public double prior

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.

balanceClasses

@SerializedName(value="balance_classes")
public boolean balanceClasses

Balance training data class counts via over/under-sampling (for imbalanced data).

classSamplingFactors

@SerializedName(value="class_sampling_factors")
public float[] classSamplingFactors

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.

maxAfterBalanceSize

@SerializedName(value="max_after_balance_size")
public float maxAfterBalanceSize

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

highestInteractionTerm

@SerializedName(value="highest_interaction_term")
public int highestInteractionTerm

Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on... Default to 2.

type

public int type

Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3

earlyStopping

@SerializedName(value="early_stopping")
public boolean earlyStopping

Stop early when there is no more relative improvement on train or validation (if provided).

saveTransformedFramekeys

@SerializedName(value="save_transformed_framekeys")
public boolean saveTransformedFramekeys

true to save the keys of transformed predictors and interaction column.

nparallelism

public int nparallelism

Number of models to build in parallel. Default to 4. Adjust according to your system.

Constructor Detail

ANOVAGLMParametersV3

public ANOVAGLMParametersV3()

Public constructor

Method Detail

toString

public java.lang.String toString()

Return the contents of this object as a JSON String.

Overrides:

toString in class ModelParametersSchemaV3