Packages

  • package root

    High level Smile operators in Scala.

    High level Smile operators in Scala.

    Definition Classes
    root
  • package smile
    Definition Classes
    root
  • package feature

    Feature generation, normalization and selection.

    Feature generation, normalization and selection.

    Feature generation (or constructive induction) studies methods that modify or enhance the representation of data objects. Feature generation techniques search for new features that describe the objects better than the attributes supplied with the training instances.

    Many machine learning methods such as Neural Networks and SVM with Gaussian kernel also require the features properly scaled/standardized. For example, each variable is scaled into interval [0, 1] or to have mean 0 and standard deviation 1. Although some method such as decision trees can handle nominal variable directly, other methods generally require nominal variables converted to multiple binary dummy variables to indicate the presence or absence of a characteristic.

    Feature selection is the technique of selecting a subset of relevant features for building robust learning models. By removing most irrelevant and redundant features from the data, feature selection helps improve the performance of learning models by alleviating the effect of the curse of dimensionality, enhancing generalization capability, speeding up learning process, etc. More importantly, feature selection also helps researchers to acquire better understanding about the data.

    Feature selection algorithms typically fall into two categories: feature ranking and subset selection. Feature ranking ranks the features by a metric and eliminates all features that do not achieve an adequate score. Subset selection searches the set of possible features for the optimal subset. Clearly, an exhaustive search of optimal subset is impractical if large numbers of features are available. Commonly, heuristic methods such as genetic algorithms are employed for subset selection.

    Definition Classes
    smile
  • Operators
t

smile.feature

Operators

trait Operators extends AnyRef

High level feature selection operators.

Linear Supertypes
AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. def +(other: String): String
    Implicit
    This member is added by an implicit conversion from Operators to any2stringadd[Operators] performed by method any2stringadd in scala.Predef.
    Definition Classes
    any2stringadd
  4. def ->[B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc
    Annotations
    @inline()
  5. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def ensuring(cond: (Operators) ⇒ Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  9. def ensuring(cond: (Operators) ⇒ Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  10. def ensuring(cond: Boolean, msg: ⇒ Any): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  11. def ensuring(cond: Boolean): Operators
    Implicit
    This member is added by an implicit conversion from Operators to Ensuring[Operators] performed by method Ensuring in scala.Predef.
    Definition Classes
    Ensuring
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. def formatted(fmtstr: String): String
    Implicit
    This member is added by an implicit conversion from Operators to StringFormat[Operators] performed by method StringFormat in scala.Predef.
    Definition Classes
    StringFormat
    Annotations
    @inline()
  16. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  17. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  22. def signalNoiseRatio(x: Array[Array[Double]], y: Array[Int]): Array[Double]

    The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems.

    The signal-to-noise (S2N) metric ratio is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. S2N is defined as |μ1 - μ2| / (σ1 + σ2), where μ1 and μ2 are the mean value of the variable in classes 1 and 2, respectively, and σ1 and σ2 are the standard deviations of the variable in classes 1 and 2, respectively. Clearly, features with larger S2N ratios are better for classification.

    References:
    • M. Shipp, et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature Medicine, 2002.
  23. def sumSquaresRatio(x: Array[Array[Double]], y: Array[Int]): Array[Double]

    The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems.

    The ratio of between-groups to within-groups sum of squares is a univariate feature ranking metric, which can be used as a feature selection criterion for multi-class classification problems. For each variable j, this ratio is BSS(j) / WSS(j) = ΣI(yi = k)(xkj - x·j)2 / ΣI(yi = k)(xij - xkj)2; where x·j denotes the average of variable j across all samples, xkj denotes the average of variable j across samples belonging to class k, and xij is the value of variable j of sample i. Clearly, features with larger sum squares ratios are better for classification.

    References:
    • S. Dudoit, J. Fridlyand and T. Speed. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc, 97:77-87, 2002.
  24. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. def [B](y: B): (Operators, B)
    Implicit
    This member is added by an implicit conversion from Operators to ArrowAssoc[Operators] performed by method ArrowAssoc in scala.Predef.
    Definition Classes
    ArrowAssoc

Inherited from AnyRef

Inherited from Any

Inherited by implicit conversion any2stringadd from Operators to any2stringadd[Operators]

Inherited by implicit conversion StringFormat from Operators to StringFormat[Operators]

Inherited by implicit conversion Ensuring from Operators to Ensuring[Operators]

Inherited by implicit conversion ArrowAssoc from Operators to ArrowAssoc[Operators]

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