scalation/scalation/scalation.modeling/Model

Model

scalation.modeling.Model

See theModel companion object

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

Companion: object
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 NullModel

class SimpleLDA

class SimpleLogisticRegression

class LogisticRegression

class SupportVectorMachine

class TANBayes

class NeuralNet_2L_Ck

class NeuralNet_3L_C2

class Forecaster_RegV

class VAR

class VARX

trait ForecasterX

class RegressionTreeGB4TS

class RegressionTreeMT4TS

class RegressionTreeRF4TS

class RegressionTreeRF_MT4TS

trait Forecast

class AR_Star

class RandomWalk_Star

class Forecaster

class AR

class ARMA

class Forecaster_D

class NeuralNet_3L4TS

class ARX_D

class NARX_SR_D

class ARX_Quad_D

class ARX_SR_D

class ARY_D

class Forecaster_Reg

class ARX

class ARX_Quad

class ARX_SR

class ARY

class ARY_Quad

class SARY

class NullModel

class RandomWalk

class RandomWalkS

class SimpleExpSmoothing

class SimpleMovingAverage

class WeightedMovingAverage

class SimpleMovingAverage2

class TrendModel

class CNN_2D

trait PredictorMV

class CNN_1D

class NeuralNet_2L

class NeuralNet_3L

class NeuralNet_XL

class NeuralNet_XLT

class RegressionMV

class RidgeRegressionMV

trait Predictor

class ClusteringPredictor

class ELM_3L1

class RegressionTree

class RegressionTreeGB

class RegressionTreeMT

class RegressionTreeRF

class RegressionTreeRF_MT

class BridgeRegression

class ExpRegression

class KNN_Regression

class LassoRegression

class SymLassoRegression

class NonlinearRegression

class NullModel

class Perceptron

class PoissonRegression

class QuantileReg

class Regression

class PolyORegression

class PolyRegression

class RegressionCat

class RegressionWLS

class RoundRegression

class SymbolicRegression

class TranRegression

class TranRegression2

class TrigRegression

class RidgeBridgeRegression

class RidgeRegression

class SymRidgeRegression

class SimpleExpRegression

class SimpleRegression

class SimplerRegression
Show all

Members list

Value members

Abstract methods

Return the best model found from feature selection.

Attributes

Return the feature/variable names.

Attributes

Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.

Attributes

Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.

Attributes

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

Attributes

Perform In-Sample Testing, i.e., train and test on the same FULL data set. Good for initial testing and understanding variable relationships. Return the prediction and the Quality of Fit.

Value parameters

showYp: whether to show the prediction vector
skip: the number of initial data points to skip (due to insufficient information)

Attributes

Note: May lead to over-fitting for complex models.

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 classes should implement various diagnostics for the test and full (training + test) datasets.

Value parameters

x_: the testing/full data/input matrix (impl. classes may default to x)
y_: the testing/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

Print the model prediction equation in readable form. Override per model.

Attributes

Print the model prediction equation in LaTex form. Override per model.

Attributes

Return the data matrix x concatenated with response vector y.

Attributes

Return the used response matrix y, if needed.

Attributes

See also: neuralnet.PredictorMV

Get the model name.

Attributes

Convert QoF results into an array (of size 1) of Statistic for compatibility with the crossValidate method.

Value parameters

qof: the Quality of Fit (QoF) results

Attributes

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

Screen the x-columns of matrix xy based on the two thresholds, returning the reduced matrix and the column indices/predictor variables selected.

Value parameters

dep: the variable/column dependency measure (defaults to correlation)
thr1: the threshold used to compare the predictor x-columns to the y-column only want variables above some minimal dependency level
thr2: the threshold used to compare the predictor x-columns with each other only want variables below some cut-off dependency/collinearity level
xy: the [ x, y ] combined data-response matrix

Attributes

Get the type of the task performed by model.

Attributes

Concrete fields

The optional reference to an ontological concept

Attributes

In this article

Generated with