scalation/scalation/scalation.modeling/FeatureSelection

FeatureSelection

scalation.modeling.FeatureSelection

See theFeatureSelection companion object

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

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

class Forecaster_Reg

class ARX

class ARX_Quad

class ARX_SR

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

class RidgeRegressionMV

trait Predictor

class ClusteringPredictor

class ELM_3L1

class RegressionTree

class RegressionTreeGB

class RegressionTreeGB4TS

class RegressionTreeMT

class RegressionTreeMT4TS

class RegressionTreeRF

class RegressionTreeRF4TS

class RegressionTreeRF_MT4TS

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
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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: indicator to include the cross-validation/validation QoF measure (defaults to "many")
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 BEAM SEARCH SELECTION to find a GOOD COMBINATION of predictive features/variables to have in the model, returning the top k sets of features/variables selected and the new Quality of Fit (QoF) measures/metrics for all steps. At each step, iterate over the models in the beam (top k) and create candidates by adding features (phase 1) and then removing features (phase 2). From all the candidates, keep the best k and start a new iteration. Stops when there is no improvement in any of top k or the maximum number of features is reached.

Value parameters

bk: the beam width holding the top k models (defaults to 3)
cross: indicator to include the cross-validation/validation QoF measure (defaults to "many")
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: indicator to include the cross-validation/validation QoF measure (defaults to "many")
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 selected 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: indicator to include the cross-validation/validation QoF measure (defaults to "many")
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: indicator to include the cross-validation/validation QoF measure (defaults to "many")
first: first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)
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)
tech: the feature selection technique to apply

Attributes

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

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