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, 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, NLS_ODE
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Inherited
  1. Model
  2. Error
  3. AnyRef
  4. Any
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Visibility
  1. Public
  2. Protected

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 flaw(method: String, message: String): Unit
    Definition Classes
    Error
  2. val modelConcept: URI

    An optional reference to an ontological concept

  3. def modelName: String

    An optional name for the model (or modeling technique)