# Loss

#### trait Loss extends Serializable

A Loss trait defines the operation needed to compute the loss function, the prediction function, and the gradient for use in a LinearModel.

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### Abstract Value Members

1. #### abstract 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

2. #### abstract 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

3. #### abstract def predict(dot: Double): Double

Computes the binary prediction based on a dot product

Computes the binary prediction based on a dot product

dot

the dot product of the linear model and the instance

returns

the predicted binary class

### Concrete 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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6. #### final def asInstanceOf[T0]: T0

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

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

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

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

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

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

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

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

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

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17. #### 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

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

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

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

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

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

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