Class

com.alpine.model.pack.ml

LinearRegressionTransformer

Related Doc: package ml

Permalink

class LinearRegressionTransformer extends RegressionTransformer

Applies a Linear Regression model specified by the coefficients and intercept to a row of numeric data.

Note that in the input row is wrapped in CastedDoubleSeq, so the input elements must be castable as java.lang.Number.

Linear Supertypes
RegressionTransformer, MLTransformer[RealResult], Transformer, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. LinearRegressionTransformer
  2. RegressionTransformer
  3. MLTransformer
  4. Transformer
  5. Serializable
  6. Serializable
  7. AnyRef
  8. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new LinearRegressionTransformer(coefficients: Seq[Double], intercept: Double = 0)

    Permalink

Type Members

  1. type Row = Seq[Any]

    Permalink

    Shorthand for the input / output type of the apply method.

    Shorthand for the input / output type of the apply method. Equivalent to Seq[Any].

    Definition Classes
    Transformer

Value Members

  1. final def !=(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. def allowNullValues: Boolean

    Permalink

    Allows the transformer to specify if the apply method can handle null values in the input row.

    Allows the transformer to specify if the apply method can handle null values in the input row.

    e.g. a Null value replacement transformer, or a Naive Bayes transformer would naturally handle null values, but a Linear Regression transformer would not.

    Default value is false.

    returns

    Boolean indicating tolerance for null values in input.

    Definition Classes
    Transformer
  5. def apply(row: Row): Row

    Permalink

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method is not required to be thread-safe.

    row

    The row of input to be scored.

    returns

    The result from applying the trained model to the row.

    Definition Classes
    RegressionTransformerTransformer
  6. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  7. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. final def eq(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  12. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  13. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  14. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  15. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  16. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  17. def predict(row: Row): Double

    Permalink
  18. def score(row: Row): RealResult

    Permalink
    Definition Classes
    RegressionTransformerMLTransformer
  19. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  20. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  21. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  23. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from RegressionTransformer

Inherited from MLTransformer[RealResult]

Inherited from Transformer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped