scalation/scalation/scalation.modeling/FitM

FitM

scalation.modeling.FitM

See theFitM companion object

trait FitM

The FitM class 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

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 NeuralNet_Class_3L

class NullModel

class SimpleLDA

class SimpleLogisticRegression

class LogisticRegression

class SupportVectorMachine

class TANBayes

trait Fit

class ClusteringPredictor

class AR1MA

class CNN_1D

class ELM_3L1

class NeuralNet_2L

class NeuralNet_3L

class NeuralNet_XL

class NeuralNet_XLT

class RegressionMV

class ExpRegression

class FitI

class AR

class ARMA

class ARIMA

class SARIMA

class SARIMAX

class NullModel

class QuadSpline

class RandomWalk

class SimpleExpSmoothing

class SimpleMovingAverage

class TrendModel

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

class TestFit
Show all

Members list

Value members

Abstract methods

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) measures provided by the Fit class. Override to correspond to 'fitLabel'.

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

Concrete methods

Diagnose the health of the model by computing the Quality of Fit (QoF) 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 Must be overridden.

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

Attributes

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

Attributes

In this article

Generated with