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    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala.

    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.

    Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

    Definition Classes
    root
  • package smile
    Definition Classes
    root
  • package validation

    Model validation.

    Model validation.

    Definition Classes
    smile
  • bootstrap
  • cv
  • loocv
  • validate

object validate

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  1. final def !=(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def classification[M <: DataFrameClassifier](formula: Formula, train: DataFrame, test: DataFrame)(trainer: (Formula, DataFrame) ⇒ M): ClassificationValidation[M]

    Test a generic classifier.

    Test a generic classifier. The accuracy will be measured and printed out on standard output.

    train

    training data.

    test

    test data.

    trainer

    a code block to return a classifier trained on the given data.

    returns

    the trained classifier.

  6. def classification[T <: AnyRef, M <: Classifier[T]](x: Array[T], y: Array[Int], testx: Array[T], testy: Array[Int])(trainer: (Array[T], Array[Int]) ⇒ M): ClassificationValidation[M]

    Test a generic classifier.

    Test a generic classifier. The accuracy will be measured and printed out on standard output.

    T

    the type of training and test data.

    x

    training data.

    y

    training labels.

    testx

    test data.

    testy

    test data labels.

    trainer

    a code block to return a classifier trained on the given data.

    returns

    the trained classifier.

  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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  16. final def notifyAll(): Unit
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  17. def regression[M <: DataFrameRegression](formula: Formula, train: DataFrame, test: DataFrame)(trainer: (Formula, DataFrame) ⇒ M): RegressionValidation[M]

    Test a generic classifier.

    Test a generic classifier. The accuracy will be measured and printed out on standard output.

    train

    training data.

    test

    test data.

    trainer

    a code block to return a classifier trained on the given data.

    returns

    the trained classifier.

  18. def regression[T <: AnyRef, M <: Regression[T]](x: Array[T], y: Array[Double], testx: Array[T], testy: Array[Double])(trainer: (Array[T], Array[Double]) ⇒ M): RegressionValidation[M]

    Test a generic classifier.

    Test a generic classifier. The accuracy will be measured and printed out on standard output.

    T

    the type of training and test data.

    x

    training data.

    y

    training labels.

    testx

    test data.

    testy

    test data labels.

    trainer

    a code block to return a classifier trained on the given data.

    returns

    the trained classifier.

  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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  21. final def wait(): Unit
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  23. final def wait(arg0: Long): Unit
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