scalation/scalation/scalation.modeling/FeatureSelection

FeatureSelection

scalation.modeling.FeatureSelection

The FeatureSelection trait establishes a framework for feature selection, i.e., selecting the features (e.g., variable x_j, cross term x_j x_k, or functional form x_j^2) to include in the model.

Attributes

Graph
Supertypes: class Object

trait Matchable

class Any
Known subtypes: class Forecaster_Reg

class ARX

class ARX_Quad

class ARX_Symb

class ARY

class ARY_Quad

class SARY

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 RegressionTreeGB4TS

class RegressionTreeMT

class RegressionTreeMT4TS

class RegressionTreeRF

class RegressionTreeRF4TS

class RegressionTreeRF_MT4TS

class RegressionTreeRF_MT

class RidgeRegression

class SimpleExpRegression

class SimpleRegression

class SimplerRegression
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Members list

Value members

Abstract methods

Perform BACKWARD ELIMINATION to find the LEAST predictive features/variables to REMOVE from the full model, returning the features/variables left and the new Quality of Fit (QoF) measures/metrics for all steps.

Value parameters

cross: whether to include the cross-validation QoF measure
first: first variable to consider for elimination
qk: index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also: Fit for index of QoF measures/metrics.

Perform FORWARD SELECTION to find the MOST predictive features/variables to ADD into the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.

Value parameters

cross: whether to include the cross-validation QoF measure
qk: index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also: Fit for index of QoF measures/metrics.

Perform STEPWISE SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the features/variables left and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, it calls forward and backward and takes the best of the two actions. Stops when neither action yields improvement.

Value parameters

cross: whether to include the cross-validation QoF measure
qk: index of Quality of Fit (QoF) to use for comparing quality
swap: whether to allow a swap step (swap out a feature for a new feature in one step)

Attributes

See also: Fit for index of QoF measures/metrics.

Concrete methods

Perform feature selection to find the most predictive features/variables to have in the model, returning the features/variables added and the new Quality of Fit (QoF) measures/metrics for all steps.

Value parameters

cross: whether to include the cross-validation QoF measure
qk: index of Quality of Fit (QoF) to use for comparing quality
tech: the feature selection technique to apply

Attributes

See also: Fit for index of QoF measures/metrics.

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