Class/Object

com.databricks.labs.automl.exploration.tools

OneDimStats

Related Docs: object OneDimStats | package tools

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class OneDimStats extends AnyRef

Package for testing a one-dimensional data set with standard data explanation metrics

Attributes tested: Mean Geometric Mean Variance Semi Variance Standard Deviation Skew Kurtosis

The return type also provides String classifications based on thresholds for Skew and Kurtosis to classify the distribution.

Since

0.7.0

Note

Kurtosis types: Mesokurtic - kurtosis around zero Leptokurtic - positive excess kurtosis (long heavy tails) Platykurtic - negative excess kurtosis (short thin tails) Skewness types: Symmetrical -> normal Asymmetricral Positive skewness -> right tailed Asymmetrical Negative skewness -> left tailed

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Instance Constructors

  1. new OneDimStats(data: Array[Double])

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    data

    Array[Double] to test one dimensional stats for

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def evaluate: OneDimStatsData

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    Public method for executing analysis of all metrics

    Public method for executing analysis of all metrics

    returns

    OneDimStatsData, containing all of the information for the analysis of the One Dimensional series of data.

    Since

    0.7.0

  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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

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  17. def toString(): String

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  18. final def wait(): Unit

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

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  20. final def wait(arg0: Long): Unit

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