Machine Learning Tools Action Set

Provides utility actions for machine learning

crossValidate Action

Action to perform cross validation with specified machine learning actions.

CASL Syntax
Summary: Output Tables
Parameter Descriptions

CASL Syntax

mlTools.crossValidate <result=results> <status=rc> /

casOut={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE",

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

kFolds=integer,

logLevel=integer,

nSubsessionWorkers=integer,

parallelFolds=TRUE | FALSE,

seed=integer,

targetEvent="string",

trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

;

indicates a required parameter

Summary: Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the score output table name and details.

Parameter Descriptions

casOut={casouttable}

specifies the score output table name and details.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

Alias	scoreTableAllFolds

kFolds=integer

specifies the number of folds to use for cross validation.

Default	5
Minimum value	2

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup summary logs added (2), fold begin and complete logs added (3).

Default	3
Range	0–3

modelType="BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GRADBOOST" | "NEURALNET" | "SVM"

specifies the model type to which cross validation is applied.

Default	DECISIONTREE

BNET

specifies cross validation for Bayesian network classifier model type.

DECISIONTREE

specifies cross validation for decision tree model type.

FACTMAC

specifies cross validation for factorization machine model type.

FOREST

specifies cross validation for forest model type.

GRADBOOST

specifies cross validation for gradient boosting tree model type.

NEURALNET

specifies cross validation for neural network model type.

SVM

specifies cross validation for support vector machine model type.

nSubsessionWorkers=integer

specifies the number of worker nodes for each subsession to use for parallel fold evaluation.

Alias	nSubWorkers
Default	0

parallelFolds=TRUE | FALSE

when set to True, evaluates folds in parallel.

Default	TRUE

seed=integer

specifies the seed to use for fold sampling for cross validation.

Default	0

targetEvent="string"

specifies the name of the nominal target event to use for model assessment.

* trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies a list of parameters for the model training action to use in the cross validation process.

crossValidate Action

Action to perform cross validation with specified machine learning actions.

Lua Syntax
Summary: Output Tables
Parameter Descriptions

Lua Syntax

results, info = s:mlTools_crossValidate{

casOut={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=true | false,

replace=true | false,

replication=integer,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE",

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

kFolds=integer,

logLevel=integer,

nSubsessionWorkers=integer,

parallelFolds=true | false,

seed=integer,

targetEvent="string",

trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

}

indicates a required parameter

Summary: Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the score output table name and details.

Parameter Descriptions

casOut={casouttable}

specifies the score output table name and details.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

Alias	scoreTableAllFolds

kFolds=integer

specifies the number of folds to use for cross validation.

Default	5
Minimum value	2

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup summary logs added (2), fold begin and complete logs added (3).

Default	3
Range	0–3

modelType="BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GRADBOOST" | "NEURALNET" | "SVM"

specifies the model type to which cross validation is applied.

Default	DECISIONTREE

BNET

specifies cross validation for Bayesian network classifier model type.

DECISIONTREE

specifies cross validation for decision tree model type.

FACTMAC

specifies cross validation for factorization machine model type.

FOREST

specifies cross validation for forest model type.

GRADBOOST

specifies cross validation for gradient boosting tree model type.

NEURALNET

specifies cross validation for neural network model type.

SVM

specifies cross validation for support vector machine model type.

nSubsessionWorkers=integer

specifies the number of worker nodes for each subsession to use for parallel fold evaluation.

Alias	nSubWorkers
Default	0

parallelFolds=true | false

when set to True, evaluates folds in parallel.

Default	true

seed=integer

specifies the seed to use for fold sampling for cross validation.

Default	0

targetEvent="string"

specifies the name of the nominal target event to use for model assessment.

* trainOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies a list of parameters for the model training action to use in the cross validation process.

crossValidate Action

Action to perform cross validation with specified machine learning actions.

Python Syntax
Summary: Output Tables
Parameter Descriptions

Python Syntax

results=s.mlTools.crossValidate(

casOut={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"promote":True | False,

"replace":True | False,

"replication":integer,

"tableRedistUpPolicy":"DEFER" | "NOREDIST" | "REBALANCE",

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

kFolds=integer,

logLevel=integer,

nSubsessionWorkers=integer,

parallelFolds=True | False,

seed=integer,

targetEvent="string",

trainOptions={"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

)

indicates a required parameter

Summary: Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the score output table name and details.

Parameter Descriptions

casOut={casouttable}

specifies the score output table name and details.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

Alias	scoreTableAllFolds

kFolds=integer

specifies the number of folds to use for cross validation.

Default	5
Minimum value	2

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup summary logs added (2), fold begin and complete logs added (3).

Default	3
Range	0–3

modelType="BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GRADBOOST" | "NEURALNET" | "SVM"

specifies the model type to which cross validation is applied.

Default	DECISIONTREE

BNET

specifies cross validation for Bayesian network classifier model type.

DECISIONTREE

specifies cross validation for decision tree model type.

FACTMAC

specifies cross validation for factorization machine model type.

FOREST

specifies cross validation for forest model type.

GRADBOOST

specifies cross validation for gradient boosting tree model type.

NEURALNET

specifies cross validation for neural network model type.

SVM

specifies cross validation for support vector machine model type.

nSubsessionWorkers=integer

specifies the number of worker nodes for each subsession to use for parallel fold evaluation.

Alias	nSubWorkers
Default	0

parallelFolds=True | False

when set to True, evaluates folds in parallel.

Default	True

seed=integer

specifies the seed to use for fold sampling for cross validation.

Default	0

targetEvent="string"

specifies the name of the nominal target event to use for model assessment.

* trainOptions={"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies a list of parameters for the model training action to use in the cross validation process.

crossValidate Action

Action to perform cross validation with specified machine learning actions.

R Syntax
Summary: Output Tables
Parameter Descriptions

R Syntax

results <– cas.mlTools.crossValidate(s,

casOut=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

tableRedistUpPolicy="DEFER" | "NOREDIST" | "REBALANCE",

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

kFolds=integer,

logLevel=integer,

nSubsessionWorkers=integer,

parallelFolds=TRUE | FALSE,

seed=integer,

targetEvent="string",

trainOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

)

indicates a required parameter

Summary: Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
casOut	—	specifies the score output table name and details.

Parameter Descriptions

casOut=list(casouttable)

specifies the score output table name and details.

For more information about specifying the casOut parameter, see the common casouttable (Form 1) parameter (Appendix A: Common Parameters).

Alias	scoreTableAllFolds

kFolds=integer

specifies the number of folds to use for cross validation.

Default	5
Minimum value	2

logLevel=integer

specifies the level of log messages to be written: no logs (0), initialization and completion logs (1), setup summary logs added (2), fold begin and complete logs added (3).

Default	3
Range	0–3

modelType="BNET" | "DECISIONTREE" | "FACTMAC" | "FOREST" | "GRADBOOST" | "NEURALNET" | "SVM"

specifies the model type to which cross validation is applied.

Default	DECISIONTREE

BNET

specifies cross validation for Bayesian network classifier model type.

DECISIONTREE

specifies cross validation for decision tree model type.

FACTMAC

specifies cross validation for factorization machine model type.

FOREST

specifies cross validation for forest model type.

GRADBOOST

specifies cross validation for gradient boosting tree model type.

NEURALNET

specifies cross validation for neural network model type.

SVM

specifies cross validation for support vector machine model type.

nSubsessionWorkers=integer

specifies the number of worker nodes for each subsession to use for parallel fold evaluation.

Alias	nSubWorkers
Default	0

parallelFolds=TRUE | FALSE

when set to True, evaluates folds in parallel.

Default	TRUE

seed=integer

specifies the seed to use for fold sampling for cross validation.

Default	0

targetEvent="string"

specifies the name of the nominal target event to use for model assessment.

* trainOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

specifies a list of parameters for the model training action to use in the cross validation process.

Last updated: November 23, 2025