 LinearRegressionTrainingSummary

class LinearRegressionTrainingSummary extends LinearRegressionSummary

:: Experimental :: Linear regression training results.

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LinearRegressionSummary, Serializable, Serializable, AnyRef, Any
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1. LinearRegressionTrainingSummary
2. LinearRegressionSummary
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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. 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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9. def equals(arg0: Any): Boolean

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10. val explainedVariance: Double

Returns the explained variance regression score.

Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: http://en.wikipedia.org/wiki/Explained_variation

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LinearRegressionSummary

12. def finalize(): Unit

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

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. val meanAbsoluteError: Double

Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

Definition Classes
LinearRegressionSummary
17. val meanSquaredError: Double

Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

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LinearRegressionSummary
18. final def ne(arg0: AnyRef): Boolean

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. val objectiveHistory: Array[Double]

objective function (scaled loss + regularization) at each iteration.

22. val r2: Double

Returns R2, the coefficient of determination.

Returns R2, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination

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LinearRegressionSummary
23. lazy val residuals: DataFrame

Residuals (label - predicted value)

Residuals (label - predicted value)

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LinearRegressionSummary
24. val rootMeanSquaredError: Double

Returns the root mean squared error, which is defined as the square root of the mean squared error.

Returns the root mean squared error, which is defined as the square root of the mean squared error.

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

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26. def toString(): String

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27. val totalIterations: Int

Number of training iterations until termination

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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