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fastIca
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Computes independent components by using the fastICA method. This action separat...

attribute
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The `attribute` action manages extended attributes for in-memory tables in CAS. ...

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The logisticLackfit action computes the Hosmer and Lemeshow goodness-of-fit test...

Random FAQ

How can I manage the randomness of fold sampling?

You can use the 'seed' parameter to specify a seed for fold sampling, which ensures reproducibility of the cross-validation process. The default value is 0.

See answer
On: crossValidate
What is weight?

Specifies the weight variable.

See answer
On: impute
model.model.whereTable.name (Required Parameter)

Specifies the name of the filter table.

See answer
On: IdentifySpeakers
How can I obtain predicted values or other statistics for each observation?

You can generate an output table with various statistics by using the 'output' parameter and specifying a CAS output table with 'casOut'. Within the 'output' parameter, you can request statistics such as predicted mean ('pred'), probability of the observed count ('prob'), and the linear predictor ('xbeta').

See answer
On: countregFitModel
model.weights.name (Required Parameter)

Specifies the name of the input table.

See answer
On: IdentifySpeakers
See full FAQ
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