RequestFrozenModel: Train a new frozen model with parameters from specified model

In datarobot: 'DataRobot' Predictive Modeling API

Train a new frozen model with parameters from specified model

Description

Frozen models use the same tuning parameters as their parent model instead of independently optimizing them to allow efficiently retraining models on larger amounts of the training data.

Usage

RequestFrozenModel(model, samplePct = NULL, trainingRowCount = NULL)

Arguments

model	An S3 object of class dataRobotModel like that returned by the function GetModel, or each element of the list returned by the function ListModels.
samplePct	Numeric, specifying the percentage of the training dataset to be used in building the new model
trainingRowCount	integer. The number of rows to use to train the requested model.

Details

Either 'sample_pct' or 'training_row_count' can be used to specify the amount of data to use, but not both. If neither are specified, a default of the maximum amount of data that can safely be used to train any blueprint without going into the validation data will be selected. In smart-sampled projects, 'samplePct' and 'trainingRowCount' are assumed to be in terms of rows of the minority class.

Note : For datetime partitioned projects, use 'RequestFrozenDatetimeModel' instead

Value

An integer value that can be used as the modelJobId parameter in subsequent calls to the GetModelFromJobId function.

An integer value that can be used as the modelJobId parameter in subsequent calls to the GetModelFromJobId function.

Examples

## Not run: 
  projectId <- "59a5af20c80891534e3c2bde"
  modelId <- "5996f820af07fc605e81ead4"
  model <- GetModel(projectId, modelId)
  RequestFrozenModel(model, samplePct = 10)

## End(Not run)

datarobot documentation built on May 29, 2024, 4:36 a.m.