# SquaredLoss

#### class SquaredLoss extends Loss with Serializable

Implementation of the squared loss function.

Linear Supertypes
Loss, Serializable, Serializable, AnyRef, Any
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1. SquaredLoss
2. Loss
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### Value Members

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

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

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

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

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protected[java.lang]
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8. #### final def eq(arg0: AnyRef): Boolean

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

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

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protected[java.lang]
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@throws( classOf[java.lang.Throwable] )
11. #### final def getClass(): Class[_]

Definition Classes
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12. #### def gradient(label: Double, dot: Double): Double

Computes the value of the gradient function

Computes the value of the gradient function

dot

the dot product of the linear model and the instance

returns

Definition Classes
SquaredLossLoss
13. #### def hashCode(): Int

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AnyRef → Any
14. #### final def isInstanceOf[T0]: Boolean

Definition Classes
Any
15. #### def loss(label: Double, dot: Double): Double

Computes the value of the loss function

Computes the value of the loss function

dot

the dot product of the linear model and the instance

returns

the loss value

Definition Classes
SquaredLossLoss
16. #### final def ne(arg0: AnyRef): Boolean

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AnyRef
17. #### final def notify(): Unit

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AnyRef
18. #### final def notifyAll(): Unit

Definition Classes
AnyRef
19. #### def predict(dot: Double): Double

Computes the binary prediction based on a dot prodcut

Computes the binary prediction based on a dot prodcut

dot

the dot product of the linear model and the instance

returns

the predicted binary class

Definition Classes
SquaredLossLoss
20. #### def prob(dot: Double): Double

Computes the probability of a binary prediction based on a dot product

Computes the probability of a binary prediction based on a dot product

dot

the dot product of the linear model and the instance

returns

the predicted probability

Definition Classes
Loss
21. #### final def synchronized[T0](arg0: ⇒ T0): T0

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AnyRef
22. #### def toString(): String

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

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

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

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