scalation/scalation/scalation.modeling/Model

Model

scalation.modeling.Model

trait Model

The Model trait provides a common framework for all models and serves as base trait for Classifier, Forecaster, Predictor, and PredictorMV traits. The train and test methods must be called first, e.g., val model = NullModel (y) model.train (null, y) model.test (null, y)

Attributes

Graph
Supertypes: class Object

trait Matchable

class Any
Known subtypes: trait Classifier

class BaggingTrees

class RandomForest

class DecisionTree_C45

class DecisionTree_C45wp

class DecisionTree_ID3

class DecisionTree_ID3wp

class HiddenMarkov

class KNN_Classifier

class LinDiscAnalyis

class NaiveBayes

class NaiveBayesR

class NeuralNet_Class_3L

class NullModel

class SimpleLDA

class SimpleLogisticRegression

class LogisticRegression

class SupportVectorMachine

class TANBayes

trait Forecaster

class AR

class AR1MA

class ARMA

class ARIMA

class SARIMA

class SARIMAX

class NullModel

class QuadSpline

class RandomWalk

class SimpleExpSmoothing

class SimpleMovingAverage

class TrendModel

trait PredictorMV

class CNN_1D

class NeuralNet_2L

class NeuralNet_3L

class NeuralNet_XL

class NeuralNet_XLT

class RegressionMV

trait Predictor

class ClusteringPredictor

class ELM_3L1

class ExpRegression

class KNN_Regression

class LassoRegression

class NonlinearRegression

class NullModel

class Perceptron

class PoissonRegression

class Regression

class ARX

class ARX_Quad

class PolyORegression

class PolyRegression

class RegressionCat

class RegressionWLS

class RoundRegression

class TranRegression

class TrigRegression

class RegressionTree

class RegressionTreeGB

class RegressionTreeMT

class RegressionTreeRF

class RidgeRegression

class SimpleExpRegression

class SimpleRegression

class SimplerRegression
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Members list

Value members

Abstract methods

Return the feature/variable names.

Attributes

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

Attributes

Return the vector of model parameter/coefficient values. Single output models have VectorD parameters, while multi-output models have MatrixD.

Attributes

Predict the value of y = f(z) by evaluating the model equation. Single output models return Double, while multi-output models return VectorD.

Value parameters

z: the new vector to predict

Attributes

Test/evaluate the model's Quality of Fit (QoF) and return the predictions and QoF vectors. This may include 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.

Value parameters

x_: the testiing/full data/input matrix (impl. classes may default to x)
y_: the testiing/full response/output vector (impl. classes may default to y)

Attributes

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'.

Value parameters

x_: the training/full data/input matrix (impl. classes may default to x)
y_: the training/full response/output vector (impl. classes may default to y)

Attributes

Concrete methods

Return a basic report on a trained and tested model.

Value parameters

ftVec: the vector of qof values produced by the Fit trait

Attributes

Return a basic report on a trained and tested multi-variate model.

Value parameters

ftMat: the matrix of qof values produced by the Fit trait

Attributes

Concrete fields

The optional reference to an ontological concept

Attributes

The name for the model (or modeling technique).

Attributes

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