case class Feature(id: Option[String], name: Option[String], projectId: Option[String], featureType: Option[String], importance: Option[Double], lowInformation: Option[Boolean], uniqueCount: Option[Int], naCount: Option[Int], dateFormat: Option[String], timeSeriesEligible: Option[Boolean], timeSeriesEligibilityReason: Option[String], timeUnit: Option[String], timeStep: Option[Int], min: Option[Double], max: Option[Double], mean: Option[Double], median: Option[Double], stdDev: Option[Double], targetLeakage: Option[String]) extends Product with Serializable
Feature
- id
(int) – the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.)
- name
(string) – feature name
- projectId
(string) – the ID of the project the feature belongs to
- featureType
(string) – feature type: ‘Numeric’, ‘Categorical’, etc.
- importance
(float) – numeric measure of the strength of relationship between the feature and target (independent of any model or other features)
- lowInformation
(bool) – whether feature has too few values to be informative
- uniqueCount
(int) – number of unique values
- naCount
(int) – number of missing values
- dateFormat
(string)–(Newinversionv2.5)thedateformatstringforhowthisfeature was interpreted (or null if not a date feature). If not null, it will be compatible with https: //docs.python.org/2/library/time.html#time.strftime .
- timeSeriesEligible
(bool) – (New in version v2.8) whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.
- timeSeriesEligibilityReason
(string)–(Newinversionv2.8)whythefeature is ineligible for time series projects, or “suitable” if it is eligible.
- timeUnit
(string) – (New in version v2.8) the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.
- timeStep
(int) – (New in version v2.8) The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.
- min
– minimum value of the EDA sample of the feature.
- max
– maximum value of the EDA sample of the feature.
- mean
– arithmetic mean of the EDA sample of the feature.
- median
– median of the EDA sample of the feature.
- stdDev
– standard deviation of EDA sample of the feature.
- targetLeakage
(int) – whether or not the feature has target leakage. ‘SKIPPED_DETECTION’ indicates leakage detection was not run on the feature, ‘FALSE’ indicates no leakage, ‘MODERATE_RISK’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage
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new
Feature(id: Option[String], name: Option[String], projectId: Option[String], featureType: Option[String], importance: Option[Double], lowInformation: Option[Boolean], uniqueCount: Option[Int], naCount: Option[Int], dateFormat: Option[String], timeSeriesEligible: Option[Boolean], timeSeriesEligibilityReason: Option[String], timeUnit: Option[String], timeStep: Option[Int], min: Option[Double], max: Option[Double], mean: Option[Double], median: Option[Double], stdDev: Option[Double], targetLeakage: Option[String])
- id
(int) – the feature ID. (Note: Throughout the API, features are specified using their names, not this ID.)
- name
(string) – feature name
- projectId
(string) – the ID of the project the feature belongs to
- featureType
(string) – feature type: ‘Numeric’, ‘Categorical’, etc.
- importance
(float) – numeric measure of the strength of relationship between the feature and target (independent of any model or other features)
- lowInformation
(bool) – whether feature has too few values to be informative
- uniqueCount
(int) – number of unique values
- naCount
(int) – number of missing values
- dateFormat
(string)–(Newinversionv2.5)thedateformatstringforhowthisfeature was interpreted (or null if not a date feature). If not null, it will be compatible with https: //docs.python.org/2/library/time.html#time.strftime .
- timeSeriesEligible
(bool) – (New in version v2.8) whether this feature can be used as a datetime partitioning feature for time series projects. Only sufficiently regular date features can be selected as the datetime feature for time series projects. Always false for non-date features. Date features that cannot be used in datetime partitioning for a time series project may be eligible for an OTV project, which has less stringent requirements.
- timeSeriesEligibilityReason
(string)–(Newinversionv2.8)whythefeature is ineligible for time series projects, or “suitable” if it is eligible.
- timeUnit
(string) – (New in version v2.8) the unit for the interval between values of this feature, e.g. DAY, MONTH, HOUR. When specifying windows for time series projects, the windows are expressed in terms of this unit. Only present for date features eligible for time series projects, and null otherwise.
- timeStep
(int) – (New in version v2.8) The minimum time step that can be used to specify time series windows. The units for this value are the timeUnit. When specifying windows for time series projects, all windows must have durations that are integer multiples of this number. Only present for date features that are eligible for time series projects and null otherwise.
- min
– minimum value of the EDA sample of the feature.
- max
– maximum value of the EDA sample of the feature.
- mean
– arithmetic mean of the EDA sample of the feature.
- median
– median of the EDA sample of the feature.
- stdDev
– standard deviation of EDA sample of the feature.
- targetLeakage
(int) – whether or not the feature has target leakage. ‘SKIPPED_DETECTION’ indicates leakage detection was not run on the feature, ‘FALSE’ indicates no leakage, ‘MODERATE_RISK’ indicates a moderate risk of target leakage, and ‘HIGH_RISK’ indicates a high risk of target leakage
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- val dateFormat: Option[String]
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final
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eq(arg0: AnyRef): Boolean
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- val featureType: Option[String]
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final
def
getClass(): Class[_]
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- def getFeatureHistogram()(implicit client: DataRobotClient): JValue
- def getMetrics()(implicit client: DataRobotClient): Map[String, Any]
- val id: Option[String]
- val importance: Option[Double]
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final
def
isInstanceOf[T0]: Boolean
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- val lowInformation: Option[Boolean]
- val max: Option[Double]
- val mean: Option[Double]
- val median: Option[Double]
- val min: Option[Double]
- val naCount: Option[Int]
- val name: Option[String]
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notify(): Unit
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def
notifyAll(): Unit
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- val projectId: Option[String]
- val stdDev: Option[Double]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
- val targetLeakage: Option[String]
- val timeSeriesEligibilityReason: Option[String]
- val timeSeriesEligible: Option[Boolean]
- val timeStep: Option[Int]
- val timeUnit: Option[String]
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def
toString(): String
- Definition Classes
- Feature → AnyRef → Any
- val uniqueCount: Option[Int]
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final
def
wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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wait(): Unit
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