This function returns a DataRobot S3 object of class dataRobotDatetimeModel for the model defined by project and modelId.
GetDatetimeModelObject(project, modelId)character. Either (1) a character string giving the unique alphanumeric identifier for the project, or (2) a list containing the element projectId with this identifier.
character. Unique alphanumeric identifier for the model of interest.
An S3 object of class `dataRobotDatetimeModel', which is a list with the following components:
featurelistId. Character string: unique alphanumeric identifier for the featurelist on which the model is based.
processes. Character vector with components describing preprocessing; may include modelType.
featurelistName. Character string giving the name of the featurelist on which the model is based.
projectId. Character string giving the unique alphanumeric identifier for the project.
samplePct. Numeric: percentage of the dataset used to form the training dataset for model fitting.
isFrozen. Logical : is model created with frozen tuning parameters.
modelType. Character string describing the model type.
metrics. List with one element for each valid metric associated with the model. Each element is a list with elements for each possible evaluation type (holdout, validation, and crossValidation).
modelCategory. Character string giving model category (e.g., blend, model).
blueprintId. Character string giving the unique DataRobot blueprint identifier on which the model is based.
modelId. Character string giving the unique alphanumeric model identifier.
projectName. Character string: optional description of project defined by projectId.
projectTarget. Character string defining the target variable predicted by all models in the project.
projectMetric. Character string defining the fitting metric optimized by all project models.
trainingRowCount. Integer or none only present for models in datetime partitioned projects. If specified, defines the number of rows used to train the model and evaluate backtest scores.
trainingDuration. Character string or none only present for models in datetime partitioned projects. If specified, a duration string specifying the duration spanned by the data used to train the model and evaluate backtest scores.
trainingStartDate. Charcter string or none only present for frozen models in datetime partitioned projects. If specified, the start date of the data used to train the model.
trainingEndDate. Charcter string or none only present for frozen models in datetime partitioned projects. If specified, the end date of the data used to train the model.
backtests. list describes what data was used to fit each backtest, the score for the project metric, and why the backtest score is unavailable if it is not provided.
dataSelectionMethod. Character string which of trainingRowCount, trainingDuration, or trainingStartDate and trainingEndDate were used to determine the data used to fit the model. One of 'rowCount', 'duration', or 'selectedDateRange'.
trainingInfo. list describes which data was used to train on when scoring the holdout and making predictions. trainingInfo will have the following keys: `holdoutTrainingStartDate`, `holdoutTrainingDuration`, `holdoutTrainingRowCount`, `holdoutTrainingEndDate`, `predictionTrainingStartDate`, `predictionTrainingDuration`, `predictionTrainingRowCount`, `predictionTrainingEndDate`. Start and end dates will be datetime string, durations will be duration strings, and rows will be integers.
holdoutScore. numeric or none the score against the holdout, if available and the holdout is unlocked, according to the project metric.
holdoutStatus. Character string the status of the holdout score, e.g. "COMPLETED", "HOLDOUT_BOUNDARIES_EXCEEDED".
If the project does not use datetime partitioning an error will occur.
# NOT RUN {
projectId <- "59a5af20c80891534e3c2bde"
modelId <- "5996f820af07fc605e81ead4"
GetDatetimeModelObject(projectId, modelId)
# }
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