water.bindings.pojos

Class ClusteringModelParametersSchema

java.lang.Object

water.bindings.pojos.Schema

water.bindings.pojos.ModelParametersSchema

water.bindings.pojos.ClusteringModelParametersSchema

Direct Known Subclasses:

KMeansParametersV3

public class ClusteringModelParametersSchema
extends ModelParametersSchema

Field Summary

Fields

Modifier and Type	Field and Description
java.lang.String	checkpoint Model checkpoint to resume training with
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 Number of clusters
boolean	keep_cross_validation_predictions Keep cross-validation model predictions
double	max_runtime_secs Maximum allowed runtime in seconds for model training.
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
boolean	parallelize_cross_validation Allow parallel training of cross-validation models
ColSpecifierV3	response_column Response column
boolean	score_each_iteration Whether to score during each iteration of model training
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
java.lang.String	validation_frame Validation frame
ColSpecifierV3	weights_column Column with observation weights

Constructor Summary

Constructors

Constructor and Description
ClusteringModelParametersSchema()

Method Summary

Methods inherited from class java.lang.Object

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

Field Detail

k

public int k

Number of clusters

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

parallelize_cross_validation

public boolean parallelize_cross_validation

Allow parallel training of cross-validation models

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)

max_runtime_secs

public double max_runtime_secs

Maximum allowed runtime in seconds for model training. Use 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

ClusteringModelParametersSchema

public ClusteringModelParametersSchema()