# LinearBinaryClassifier

#### class LinearBinaryClassifier extends BinaryClassifier[la.Tensor1] with model.Parameters with OptimizablePredictor[Double, la.Tensor1]

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1. LinearBinaryClassifier
2. OptimizablePredictor
3. Parameters
4. BinaryClassifier
5. Classifier
6. Predictor
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### 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. #### def Weights(t4: ⇒ Tensor4): Weights4

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7. #### def Weights(t3: ⇒ Tensor3): Weights3

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8. #### def Weights(t2: ⇒ Tensor2): Weights2

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9. #### def Weights(t1: ⇒ Tensor1): Weights1

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10. #### def accumulateObjectiveGradient(accumulator: WeightsMapAccumulator, features: la.Tensor1, gradient: Double, weight: Double): Unit

Put gradient of objective with respect to parameters into the accumulator.

Put gradient of objective with respect to parameters into the accumulator. The contract states we cannot mutate the "input" argument inside this method.

accumulator

weight

Definition Classes
LinearBinaryClassifierOptimizablePredictor
11. #### final def asInstanceOf[T0]: T0

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12. #### def classification(input: la.Tensor1): BinaryClassification

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BinaryClassifierClassifier
13. #### def clone(): AnyRef

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

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

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

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

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

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

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

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

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

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24. #### val parameters: WeightsSet

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25. #### def predict(features: la.Tensor1): Double

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

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

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

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

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

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