scalation/scalation/scalation.modeling/FitM

FitM

scalation.modeling.FitM

See theFitM companion object

trait FitM

The FitM trait provides methods to determine basic Quality of Fit 'QoF' metrics/measures suitable for all Models. Note, to work with multiple types of models where degrees of freedom (df) may be hard to calculate, sde uses m-1 rather than df for sample estimates, while rmse uses a population formula (i.e., divide by m). Therefore, in ScalaTion sde will be slightly larger than rmse.

Attributes

See also: Fit for a more complete implementation suitable for several models.
Companion: object
Graph
Supertypes: class Object

trait Matchable

class Any
Known subtypes: trait FitC

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 RNN

class Baseline

trait Fit

class ClusteringPredictor

class Diagnoser

class AR_Star

class RandomWalk_Star

class VAR

class Forecaster

class AR

class ARMA

class ARIMA

class Forecaster_D

class NeuralNet_3L4TS

class ARX_D

class ARX_Quad_D

class ARX_Symb_D

class ARY_D

class Forecaster_Reg

class ARX

class ARX_Quad

class ARX_Symb

class ARY

class ARY_Quad

class SARY

class NullModel

class RandomWalk

class RandomWalkS

class SimpleExpSmoothing

class SimpleMovingAverage

class TranARY

class TrendModel

class WeightedMovingAverage

class AR

class AR1MA

class ARIMA

class ARMA

class NullModel

class QuadSpline

class RandomWalk

class SimpleExpSmoothing

class SimpleMovingAverage

class TrendModel

class WeightedMovingAverage

class CNN_1D

class CNN_2D

class ELM_3L1

class NeuralNet_2L

class NeuralNet_3L

class NeuralNet_XL

class NeuralNet_XLT

class RegressionMV

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 RegressionTreeGB4TS

class RegressionTreeMT

class RegressionTreeMT4TS

class RegressionTreeRF

class RegressionTreeRF4TS

class RegressionTreeRF_MT4TS

class RegressionTreeRF_MT

class RidgeRegression

class SimpleExpRegression

class SimpleRegression

class SimplerRegression

class TestFit
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Members list

Value members

Concrete methods

Diagnose and return the health of the model by computing the Quality of Fit (QoF) metrics/measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

Value parameters

w: the weights on the instances (defaults to null)
y: the actual response/output vector to use (test/full)
yp: the predicted response/output vector (test/full)

Attributes

See also: Regression_WLS Override to add more metrics.

Return the Quality of Fit (QoF) measures corresponding to the labels given. Note, if sse > sst, the model introduces errors and the rSq may be negative, otherwise, R^2 (rSq) ranges from 0 (weak) to 1 (strong). Override to add more quality of fit measures.

Attributes

Return the help string that describes the Quality of Fit (QoF) metrics/measures.

Attributes

See also: Fit for an implementation. Override to correspond to fitLabel.

Return the coefficient of determination (R^2). Must call diagnose first.

Attributes

Return the symmetric Mean Absolute Percentage Error (sMAPE) score. Caveat: y_i = yp_i = 0 => no error => no percentage error

Value parameters

e_: the error/residual vector (if null, recompute)
y: the given time-series (must be aligned with the forecast)
yp: the forecasted time-series

Attributes

Return the sum of the squares for error (sse). Must call diagnose first.

Attributes

Produce a QoF summary for a model with diagnostics for each predictor 'x_j' and the overall Quality of Fit (QoF).

Value parameters

b: the parameters/coefficients for the model
fname: the array of feature/variable names
vifs: the Variance Inflation Factors (VIFs)
x_: the testing/full data/input matrix

Attributes

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