Packages

trait Model extends Error

The Model trait provides a common framework for all analytics models and serves as base trait for Classifier and Predcitor traits. The 'train' and 'eval' methods must be called first, e.g.,

val model = NullModel (y) model.train (null, y).eval (null, y)

Linear Supertypes
Error, AnyRef, Any
Known Subclasses
ANCOVA, ANCOVA1, ANOVA1, ConvNet_1D, CubicRegression, CubicXRegression, ELM_3L, ELM_3L1, ExpRegression, KNN_Predictor, LassoRegression, MV_Regression, NeuralNet_2L, NeuralNet_3L, NeuralNet_3L1, NeuralNet_XL, NeuralNet_XLT, NonLinRegression, NullModel, Perceptron, PoissonRegression, PolyORegression, PolyRegression, Predictor, PredictorMat, PredictorMat2, QuadELM_3L1, QuadRegression, QuadXELM_3L1, QuadXRegression, Regression, RegressionTree, RegressionTree_GB, Regression_WLS, RidgeRegression, RoundRegression, SimpleExpRegression, SimpleRegression, SimplerRegression, TranRegression, TrigRegression, BayesClassifier, BayesClfML, BayesNetwork, Classifier, ClassifierInt, ClassifierReal, DecisionTreeC45, DecisionTreeC45wp, DecisionTreeID3, DecisionTreeID3wp, FANBayes, FANBayes0, GMM, HiddenMarkov, KNN_Classifier, LDA, LogisticRegression, NaiveBayes, NaiveBayes0, NaiveBayesR, NeuralNet_Classif_3L, NeuralNet_Classif_XL, NullModel, OneBAN, OneBAN0, PGMHD3, PGMHD3cp, PGMHD3fl, RandomForest, RandomForest2, SimpleLDA, SimpleLogisticRegression, SupportVectorMachine, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, BayesClassifier, NaiveBayes, NaiveBayes0, TANBayes, TANBayes0, TwoBAN_OS, TwoBAN_OS0, ClusteringPredictor, Regression_F2S, AR, ARIMA, ARMA, AR_1, AR_2, ELM_3L1_4TS, ExpSmoothing, ForecasterVec, MA, MA_1, MovingAverage, MovingAverageD, NeuralNet_3L1_4TS, NeuralNet_3L1_4TSy, NullModel, QuadRegression4TS, QuadRegression4TSy, QuadSpline, QuadXRegression4TS, QuadXRegression4TSy, RandomWalk, Regression4TS, Regression4TSy, SARIMA, SARIMAX, SimpleExpSmoothing, ANCOVA, PolyRegression, PredictorVec, Regression, RidgeRegression, NLS_ODE
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  1. Model
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Abstract Value Members

  1. abstract def eval(x_e: MatriD, y_e: VectoD): Model

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model).

    Evaluate the model's Quality of Fit (QoF) as well as the importance of its parameters (e.g., if 0 is in a parameter's confidence interval, it is a candidate for removal from the model). Extending traits and classess should implement various diagnostics for the test and full (training + test) datasets.

    x_e

    the test/full data/input matrix (impl. classes should default to x)

    y_e

    the test/full response/output vector (impl. classes should default to y)

  2. abstract def hparameter: HyperParameter

    Return the model hyper-parameters (if none, return null).

    Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

  3. abstract def parameter: VectoD

    Return the vector of model parameter/coefficient values.

  4. abstract def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    See also

    'summary' method for more details

  5. abstract def train(x_: MatriD, y_: VectoD): Model

    Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.

    Train the model 'y_ = f(x_) + e' on a given dataset, by optimizing the model parameters in order to minimize error '||e||' or maximize log-likelihood 'll'.

    x_

    the training/full data/input matrix (impl. classes should default to x)

    y_

    the training/full response/output vector (impl. classes should default to y)

Concrete Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[lang]
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. final def flaw(method: String, message: String): Unit
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  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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  11. final def isInstanceOf[T0]: Boolean
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  12. val modelConcept: URI

    An optional reference to an ontological concept

  13. def modelName: String

    An optional name for the model (or modeling technique)

  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
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  16. final def notifyAll(): Unit
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  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. def toString(): String
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  19. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  20. final def wait(arg0: Long): Unit
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    @throws( ... ) @native()
  21. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
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    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated

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