Machine Learning Tools Action Set
Provides utility actions for machine learning
crossValidate Action
Action to perform cross validation with specified machine learning actions.
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
— |
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 |
|---|
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.
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
— |
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 |
|---|
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.
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
— |
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 |
|---|
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.
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.
|
Parameter |
Subparameter |
Description |
|---|---|---|
|
— |
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 |
|---|
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.