Model

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def advancedTuning(description: String)(implicit client: DataRobotClient): AdvancedTuningSession

final def asInstanceOf[T0]: T0

val blueprintId: String

the blueprint used to construct the model - note this is not an ObjectId

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

val featurelistId: String

the ID of the featurelist used by the model

val featurelistName: String

the name of the featurelist used by the model

def getCapabilities()(implicit client: DataRobotClient): (Map[String, Boolean], Map[String, String])

final def getClass(): Class[_]

def getCrossValidationScores()(implicit client: DataRobotClient): Map[String, Map[String, Map[String, Double]]]

def getFeatureEffects(source: String = "validation", backtestIndex: Option[String] = None)(implicit client: DataRobotClient): FeatureEffects

def getFeatureEffectsMetaData()(implicit client: DataRobotClient): String

def getFeatureFit(source: String = "validation", backtestIndex: Option[String] = None)(implicit client: DataRobotClient): FeatureFits

def getFeatureFitMetaData()(implicit client: DataRobotClient): String

def getFeatureImpact()(implicit client: DataRobotClient): FeatureImpacts

def getHyperParameters()(implicit client: DataRobotClient): Map[String, List[Map[String, Any]]]

def getLiftChart(source: enums.Source.Value)(implicit client: DataRobotClient): LiftChart

def getLiftCharts()(implicit client: DataRobotClient): Map[String, List[LiftChart]]

def getMissingValueReport()(implicit client: DataRobotClient): List[Map[_ <: String, Any]]

def getModelBlueprintChart()(implicit client: DataRobotClient): Graph[BlueprintNode, LDiEdge]

def getModelCoefficients()(implicit client: DataRobotClient): ModelCoefficients

def getResiduals()(implicit client: DataRobotClient): Map[String, Map[String, ResidualData]]

def getRocCurve(source: enums.Source.Value)(implicit client: DataRobotClient): RocCurve

def getRocCurves()(implicit client: DataRobotClient): Map[String, List[RocCurve]]

def getScoringCode(destination: Option[String] = None, sourceCode: Boolean = false)(implicit client: DataRobotClient): Unit

def getWordCloud()(implicit client: DataRobotClient): WordCloud

def hashCode(): Int

val id: String

Model ID

val isFrozen: Boolean

boolean, indicating whether the model is frozen, i.e.

final def isInstanceOf[T0]: Boolean

var isStarred: Boolean

(bool) (New in version v2.13) whether the model has been starred

val metrics: Map[String, Metric]

the performance of the model according to various metrics, see below

val modelCategory: String

indicateswhatkindofmodelitis -willbeprimeforDataRobotPrime models, blend for blender models, scaleout for scaleout models, and model for all other models

val modelType: String

identifies the model, e.g.

val monotonicDecreasingFeaturelistId: Option[String]

(new in v2.11) null or str, the ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target.

val monotonicIncreasingFeaturelistId: Option[String]

(new in v2.11) null or str, the ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target.

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var predictionThreshold: Option[Double]

(float) (New in version v2.13) threshold used for binary classification in predictions.

var predictionThresholdReadOnly: Option[Boolean]

(boolean) (New in version v2.13) indicates whether modification of a predictions threshold is forbidden.

val processes: Array[String]

a json list of processes used by the model

val projectId: String

the ID of the project to which the model belongs

def requestAndGetFeatureEffects(source: String = "validation", backtestIndex: Option[String] = None)(implicit client: DataRobotClient): FeatureEffects

def requestAndGetFeatureFit(source: String = "validation", backtestIndex: Option[String] = None)(implicit client: DataRobotClient): FeatureFits

def requestAndGetFeatureImpact(maxWait: Int = 600000)(implicit client: DataRobotClient): AnyRef

def requestFeatureEffects(backtestIndex: Option[String] = None)(implicit client: DataRobotClient): Job

def requestFeatureFit(backtestIndex: Option[String] = None)(implicit client: DataRobotClient): Job

def requestFeatureImpact()(implicit client: DataRobotClient): Job

def requestFrozenModel(samplePct: Option[Float] = None, trainingRowCount: Option[Int] = None)(implicit client: DataRobotClient): ModelJob

def requestPredictions(datasetId: String, includePredictionIntervals: Option[Boolean] = None, predictionIntervalSize: Option[Int] = None, forecastPoint: Option[String] = None, predictionsStartDate: Option[String] = None, predictionsEndDate: Option[String] = None)(implicit client: DataRobotClient): PredictJob

def runCrossValidation()(implicit client: DataRobotClient): Job

val samplePct: Option[Double]

the percentage of the dataset used in training the model

def starModel()(implicit client: DataRobotClient): Model

val supportsMonotonicConstraints: Option[Boolean]

(new in v2.11) boolean, whether this model supports enforcing montonic constraints

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def toggleStar(starred: Boolean)(implicit client: DataRobotClient): Model

val trainingDuration: Option[String]

the duration spanned by the dates in the partition column for the data used to train the model

val trainingEndDate: Option[String]

the end date of the dates in the partition column for the data used to train the model

val trainingRowCount: Option[Int]

the number of rows used to train the model

val trainingStartDate: Option[String]

the start date of the dates in the partition column for the data used to train the model

def unstarModel()(implicit client: DataRobotClient): Model

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

final def wait(): Unit

def finalize(): Unit