datarobot (version 2.18.6)

RequestSampleSizeUpdate: Refits an existing model to a different fraction of the training dataset

Description

This function requests a refit of the model defined by the model parameter to the same training dataset used in building it originally, but with a different fraction of the data, specified by the samplePct parameter. The function returns an integer value that may be used with the function GetModelFromJobId to retrieve the model after fitting is complete.

Usage

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

Value

Integer, value to be used as the modelJobId parameter in calling the function GetModelFromJobId to retrieve the updated model.

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

Motivation for this function is the fact that some models - e.g., very complex machine learning models fit to large datasets - may take a long time to complete. Splitting the model creation request from model retrieval in these cases allows the user to perform other interactive R session tasks between the time the model creation/update request is made and the time the final model is available.

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.

Examples

if (FALSE) {
  projectId <- "59a5af20c80891534e3c2bde"
  modelId <- "5996f820af07fc605e81ead4"
  model <- GetModel(projectId, modelId)
  RequestSampleSizeUpdate(model, samplePct = 100)
}