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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 cv

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  1. final def !=(arg0: Any): Boolean
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  5. def classification[M <: DataFrameClassifier](k: Int, formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => M): ClassificationValidations[M]

    Cross validation on a data frame classifier.

    Cross validation on a data frame classifier.

    k

    k-fold cross validation.

    formula

    model formula.

    data

    data samples.

    trainer

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

    returns

    metric scores.

  6. def classification[T <: AnyRef, M <: Classifier[T]](k: Int, x: Array[T], y: Array[Int])(trainer: (Array[T], Array[Int]) => M): ClassificationValidations[M]

    Cross validation on a generic classifier.

    Cross validation on a generic classifier. Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds.

    k

    k-fold cross validation.

    x

    data samples.

    y

    sample labels.

    trainer

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

    returns

    metric scores.

  7. def clone(): AnyRef
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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  8. final def eq(arg0: AnyRef): Boolean
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  10. def finalize(): Unit
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  17. def regression[M <: DataFrameRegression](k: Int, formula: Formula, data: DataFrame)(trainer: (Formula, DataFrame) => M): RegressionValidations[M]

    Cross validation on a data frame regression model.

    Cross validation on a data frame regression model.

    k

    k-fold cross validation.

    formula

    model formula.

    data

    data samples.

    trainer

    a code block to return a regression model trained on the given data.

    returns

    metric scores.

  18. def regression[T <: AnyRef, M <: Regression[T]](k: Int, x: Array[T], y: Array[Double])(trainer: (Array[T], Array[Double]) => M): RegressionValidations[M]

    Cross validation on a generic regression model.

    Cross validation on a generic regression model.

    k

    k-fold cross validation.

    x

    data samples.

    y

    response variable.

    trainer

    a code block to return a regression model trained on the given data.

    returns

    metric scores.

  19. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  20. def toString(): String
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  21. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  22. final def wait(arg0: Long, arg1: Int): Unit
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  23. final def wait(arg0: Long): Unit
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