water.bindings.pojos

Class PCAParametersV3

java.lang.Object

water.bindings.pojos.Schema

water.bindings.pojos.ModelParametersSchema

water.bindings.pojos.PCAParametersV3

public class PCAParametersV3
extends ModelParametersSchema

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String	checkpoint Model checkpoint to resume training with
boolean	compute_metrics Whether to compute metrics on the training data
FoldAssignmentScheme	fold_assignment Cross-validation fold assignment scheme, if fold_column is not specified
ColSpecifierV3	fold_column Column with cross-validation fold index assignment per observation
boolean	ignore_const_cols Ignore constant columns
java.lang.String[]	ignored_columns Ignored columns
int	k Rank of matrix approximation
boolean	keep_cross_validation_predictions Keep cross-validation model predictions
int	max_iterations Maximum training iterations
java.lang.String	model_id Destination id for this model; auto-generated if not specified
int	nfolds Number of folds for N-fold cross-validation
ColSpecifierV3	offset_column Offset column
Method	pca_method Method for computing PCA (Caution: Power and GLRM are currently experimental and unstable)
ColSpecifierV3	response_column Response column
boolean	score_each_iteration Whether to score during each iteration of model training
long	seed RNG seed for initialization
StoppingMetric	stopping_metric Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
int	stopping_rounds Early stopping based on convergence of stopping_metric.
double	stopping_tolerance Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
java.lang.String	training_frame Training frame
TransformType	transform Transformation of training data
boolean	use_all_factor_levels Whether first factor level is included in each categorical expansion
java.lang.String	validation_frame Validation frame
ColSpecifierV3	weights_column Column with observation weights

Constructor Summary

Constructors

Constructor and Description
PCAParametersV3()

Method Summary

Methods inherited from class java.lang.Object

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

Field Detail

transform

public TransformType transform

Transformation of training data

pca_method

public Method pca_method

Method for computing PCA (Caution: Power and GLRM are currently experimental and unstable)

k

public int k

Rank of matrix approximation

max_iterations

public int max_iterations

Maximum training iterations

seed

public long seed

RNG seed for initialization

use_all_factor_levels

public boolean use_all_factor_levels

Whether first factor level is included in each categorical expansion

compute_metrics

public boolean compute_metrics

Whether to compute metrics on the training data

model_id

public java.lang.String model_id

Destination id for this model; auto-generated if not specified

training_frame

public java.lang.String training_frame

Training frame

validation_frame

public java.lang.String validation_frame

Validation frame

nfolds

public int nfolds

Number of folds for N-fold cross-validation

keep_cross_validation_predictions

public boolean keep_cross_validation_predictions

Keep cross-validation model predictions

response_column

public ColSpecifierV3 response_column

Response column

weights_column

public ColSpecifierV3 weights_column

Column with observation weights

offset_column

public ColSpecifierV3 offset_column

Offset column

fold_column

public ColSpecifierV3 fold_column

Column with cross-validation fold index assignment per observation

fold_assignment

public FoldAssignmentScheme fold_assignment

Cross-validation fold assignment scheme, if fold_column is not specified

ignored_columns

public java.lang.String[] ignored_columns

Ignored columns

ignore_const_cols

public boolean ignore_const_cols

Ignore constant columns

score_each_iteration

public boolean score_each_iteration

Whether to score during each iteration of model training

checkpoint

public java.lang.String checkpoint

Model checkpoint to resume training with

stopping_rounds

public int stopping_rounds

Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)

stopping_metric

public StoppingMetric stopping_metric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)

stopping_tolerance

public double stopping_tolerance

Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)

Constructor Detail

PCAParametersV3

public PCAParametersV3()