# OptimizableObjectives

#### object OptimizableObjectives

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### Type Members

2. #### type Binary = UnivariateOptimizableObjective[Int]

General type for objective functions for binary classification

13. #### type Multiclass = MultivariateOptimizableObjective[Int]

General type for multivariate linear objective functions for clasification

### Value Members

1. #### final def !=(arg0: AnyRef): Boolean

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2. #### final def !=(arg0: Any): Boolean

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3. #### final def ##(): Int

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4. #### final def ==(arg0: AnyRef): Boolean

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5. #### final def ==(arg0: Any): Boolean

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6. #### val absoluteUnivariate: AbsoluteUnivariate

Absolute objective for univariate regression

7. #### final def asInstanceOf[T0]: T0

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8. #### def clone(): AnyRef

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9. #### def epsilonInsensitiveAbsMultivariate(epsilon: Double): EpsilonInsensitiveAbsMultivariate

Epsilon-insensitive absolute objective for multivariate regression

Epsilon-insensitive absolute objective for multivariate regression

epsilon

The tolerance of the objective function

returns

An objective function

10. #### def epsilonInsensitiveAbsUnivariate(epsilon: Double): EpsilonInsensitiveAbsUnivariate

Epsilon-insensitive absolute objective for univariate regression

Epsilon-insensitive absolute objective for univariate regression

epsilon

The tolerance of the objective function

returns

An objective function

11. #### def epsilonInsensitiveSqMultivariate(epsilon: Double): EpsilonInsensitiveSqMultivariate

Epsilon-insensitive squared objective for multivariate regression

Epsilon-insensitive squared objective for multivariate regression

epsilon

The tolerance of the objective function

returns

An objective function

12. #### def epsilonInsensitiveSqUnivariate(epsilon: Double): EpsilonInsensitiveSqUnivariate

Epsilon-insensitive squared objective for univariate regression

Epsilon-insensitive squared objective for univariate regression

epsilon

The tolerance of the objective function

returns

An objective function

13. #### final def eq(arg0: AnyRef): Boolean

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14. #### def equals(arg0: Any): Boolean

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15. #### def finalize(): Unit

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16. #### final def getClass(): Class[_]

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17. #### def hashCode(): Int

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18. #### val hingeBinary: HingeBinary

Hinge objective for binary classification

19. #### val hingeMulticlass: HingeMulticlass

Hinge objective for multiclass classification

20. #### def hingeScaledBinary(posCost: Double = 1.0, negCost: Double = 1.0): HingeScaledBinary

A variant of the hinge objective for binary classification which can have different costs for type 1 and type 2 errors.

A variant of the hinge objective for binary classification which can have different costs for type 1 and type 2 errors.

posCost

The cost of predicting positive when the label is negative

negCost

The cost of predicting negative when the label is positive

returns

An objective function

21. #### val hingeSqMulticlass: HingeSqMulticlass

Squared hinge objective for multiclass classification

22. #### final def isInstanceOf[T0]: Boolean

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23. #### val logBinary: LogBinary

Log objective for binary classification

24. #### val logMulticlass: LogMulticlass

Log objective for multiclass classification.

Log objective for multiclass classification. Inefficient.

25. #### val logisticLinkFunction: (Double) ⇒ Double

The logistic sigmoid function.

26. #### final def ne(arg0: AnyRef): Boolean

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27. #### final def notify(): Unit

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28. #### final def notifyAll(): Unit

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29. #### def smoothHingeBinary(gamma: Double = 1.0, margin: Double = 1.0, posCost: Double = 1.0, negCost: Double = 1.0): SmoothHingeBinary

A smoothed (Lipschitz gradient) variant of the hinge objective for binary classification which can have different costs for type 1 and type 2 errors and adjustable margin.

A smoothed (Lipschitz gradient) variant of the hinge objective for binary classification which can have different costs for type 1 and type 2 errors and adjustable margin.

gamma

Adjusts how smoothly the hinge drops down to zero. Higher is more smooth, zero gives unsmoothed hinge.

margin

The number that you need to predict above to achieve the maximum objective score.

posCost

The cost of predicting positive when the label is negative.

negCost

The cost of predicting negative when the label is positive.

returns

An objective function

30. #### val sparseLogMulticlass: SparseLogMulticlass

Sparse Log objective for multiclass classification; very efficient.

32. #### val squaredMultivariate: SquaredMultivariate

Squared objective for multivariate regression

33. #### val squaredUnivariate: SquaredUnivariate

Squared objective for univariate regression

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

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35. #### def toString(): String

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36. #### final def wait(): Unit

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

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38. #### final def wait(arg0: Long): Unit

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