A rating table is an exportable CSV representation of a Generalized Additive Model. It contains information about the features and coefficients used to make predictions. Users can influence predictions by downloading and editing values in a rating table, then uploading the table and using it to create a new model. See the page about interpreting Generalized Additive Model output in the Datarobot user guide for more details on how to interpret and edit rating tables.

## Connect to DataRobot

To explore rating tables, let's first connect to DataRobot. First, you must load the DataRobot R package library.

If you have set up a credentials file, `library(datarobot)` will initialize a connection to DataRobot automatically. Otherwise, you can specify your `endpoint` and `apiToken` as in this example to connect to DataRobot directly. For more information on connecting to DataRobot, see the “Introduction to DataRobot” vignette.

    library(datarobot)
    endpoint <- "https://<YOUR DATAROBOT URL GOES HERE>/api/v2"
    apiToken <- "<YOUR API TOKEN GOES HERE>"
    ConnectToDataRobot(endpoint = endpoint, token = apiToken)

## Retrieving Rating Tables

You can retrieve a rating table from the list of rating tables in a project:

    projectId <- "59dab74bbd2a54035786bfc0"
    ratingTables <- ListRatingTables(projectId)
    ratingTable <- ratingTables[[1]]
    print(ratingTable)

$validationJobId NULL

$validationError [1] “”

$projectId [1] “59dab74bbd2a54035786bfc0”

$ratingTableName [1] “Rating Table for 59dab774bd2a54035d157fa7”

$parentModelId [1] “59dab774bd2a54035d157fa7”

$modelJobId NULL

$id [1] “59dab7a06f42a6df428bc14c”

$originalFilename [1] “rating_table.csv”

$modelId [1] “59dab774bd2a54035d157fa7”

attr(,“class”) [1] “dataRobotRatingTable”

Or you can retrieve a rating table from a specific model. The model must already have a rating table.

    projectId <- "59dab74bbd2a54035786bfc0"
    ratingTableModels <- ListRatingTableModels(projectId)
    ratingTableModel <- ratingTableModels[[1]]
    ratingTableId <- ratingTableModel$ratingTableId
    ratingTable <- GetRatingTable(projectId, ratingTableId)
    print(ratingTable)

$validationJobId NULL

$validationError [1] “”

$projectId [1] “59dab74bbd2a54035786bfc0”

$ratingTableName [1] “Rating Table for 59dab774bd2a54035d157fa7”

$parentModelId [1] “59dab774bd2a54035d157fa7”

$modelJobId NULL

$id [1] “59dab7a06f42a6df428bc14c”

$originalFilename [1] “rating_table.csv”

$modelId [1] “59dab774bd2a54035d157fa7”

attr(,“class”) [1] “dataRobotRatingTable”

Or retrieve model by id. The model must have a rating table.

    projectId <- "59dab74bbd2a54035786bfc0"
    modelId <- "59dd0b01d9575702bec96e4"
    ratingTableModel <- GetRatingTableModel(projectId, modelId)
    ratingTableId <- ratingTableModel$ratingTableId
    ratingTable <- GetRatingTable(projectId, ratingTableId)
    print(ratingTable)

$validationJobId NULL

$validationError [1] “”

$projectId [1] “59dab74bbd2a54035786bfc0”

$ratingTableName [1] “Rating Table for 59dab774bd2a54035d157fa7”

$parentModelId [1] “59dab774bd2a54035d157fa7”

$modelJobId NULL

$id [1] “59dab7a06f42a6df428bc14c”

$originalFilename [1] “rating_table.csv”

$modelId [1] “59dab774bd2a54035d157fa7”

attr(,“class”) [1] “dataRobotRatingTable”

## Downloading Rating Tables

Once you have a rating table, you can download the contents to a CSV.

    DownloadRatingTable(projectId, ratingTableId, "myRatingTable.csv")

## Modifying Rating Tables

You can then modify the values in the CSV and re-upload a new rating table back to DataRobot.

    DownloadRatingTable(projectId, ratingTableId, "myRatingTable.csv")
    newRatingTableJobId <- CreateRatingTable(project,
                                             modelId,
                                             "myRatingTable.csv",
                                             ratingTableName = "Modified File")
    newRatingTable <- GetRatingTableFromJobId(project, newRatingTableJobId)
    print(newRatingTable)

$validationJobId NULL

$validationError [1] “”

$projectId [1] “59dab74bbd2a54035786bfc0”

$ratingTableName [1] “Rating Table for 59dab774bd2a54035d157fa7”

$parentModelId [1] “59dab774bd2a54035d157fa7”

$modelJobId NULL

$id [1] “59dab7a06f42a6df428bc14c”

$originalFilename [1] “rating_table.csv”

$modelId [1] “59dab774bd2a54035d157fa7”

attr(,“class”) [1] “dataRobotRatingTable”

## Making New GAMs from New Rating Tables

You can then take the new rating tables you make and create new models from them.

    newModelJobId <- RequestNewRatingTableModel(project, newRatingTable)
    newRatingTableModel <- GetRatingTableModelFromJobId(project, newModelJobId)
    print(newRatingTableModel)

$featurelistId [1] “59dd4731c0d33327b8f55610”

$processes [1] “One-Hot Encoding”
[2] “Ordinal encoding of categorical variables”
[3] “Missing Values Imputed”
[4] “Matrix of word-grams occurrences”
[5] “Generalized Additive Model”
[6] “Text fit on Residuals (L2 / Binomial Deviance)”

$featurelistName [1] “Informative Features”

$projectId [1] “59dd4723d957570407bc37b4”

$modelType [1] “Generalized Additive Model”

$samplePct [1] 64.041

$isFrozen [1] FALSE

$metrics $metrics$AUC $metrics$AUC$backtesting NULL

$metrics$AUC$holdout NULL

$metrics$AUC$backtestingScores NULL

$metrics$AUC$crossValidation NULL

$metrics$AUC$validation [1] 0.77283

$metrics$`Rate@Top5%` $metrics$`Rate@Top5%`$backtesting NULL

$metrics$`Rate@Top5%`$holdout NULL

$metrics$`Rate@Top5%`$backtestingScores NULL

$metrics$`Rate@Top5%`$crossValidation NULL

$metrics$`Rate@Top5%`$validation [1] 1

$metrics$`Rate@TopTenth%` $metrics$`Rate@TopTenth%`$backtesting NULL

$metrics$`Rate@TopTenth%`$holdout NULL

$metrics$`Rate@TopTenth%`$backtestingScores NULL

$metrics$`Rate@TopTenth%`$crossValidation NULL

$metrics$`Rate@TopTenth%`$validation [1] 1

$metrics$RMSE $metrics$RMSE$backtesting NULL

$metrics$RMSE$holdout NULL

$metrics$RMSE$backtestingScores NULL

$metrics$RMSE$crossValidation NULL

$metrics$RMSE$validation [1] 0.41992

$metrics$LogLoss $metrics$LogLoss$backtesting NULL

$metrics$LogLoss$holdout NULL

$metrics$LogLoss$backtestingScores NULL

$metrics$LogLoss$crossValidation NULL

$metrics$LogLoss$validation [1] 0.50643

$metrics$`FVE Binomial` $metrics$`FVE Binomial`$backtesting NULL

$metrics$`FVE Binomial`$holdout NULL

$metrics$`FVE Binomial`$backtestingScores NULL

$metrics$`FVE Binomial`$crossValidation NULL

$metrics$`FVE Binomial`$validation [1] 0.23955

$metrics$`Gini Norm` $metrics$`Gini Norm`$backtesting NULL

$metrics$`Gini Norm`$holdout NULL

$metrics$`Gini Norm`$backtestingScores NULL

$metrics$`Gini Norm`$crossValidation NULL

$metrics$`Gini Norm`$validation [1] 0.54566

$metrics$`Rate@Top10%` $metrics$`Rate@Top10%`$backtesting NULL

$metrics$`Rate@Top10%`$holdout NULL

$metrics$`Rate@Top10%`$backtestingScores NULL

$metrics$`Rate@Top10%`$crossValidation NULL

$metrics$`Rate@Top10%`$validation [1] 1

$modelCategory [1] “model”

$blueprintId [1] “24ea7590216323555b8fe51fe006dfba”

$ratingTableId [1] “59dd4778e643feefd5d87c9b”

$id [1] “59dd474bd95757040120a8e8”

attr(,“class”) [1] “dataRobotRatingTableModel”