Features selection and importance

Feature selection

SDeMo.VariableSelectionStrategy Type

VariableSelectionStrategy

This is an abstract type to which all variable selection types belong. The variable selection methods should define a method for variables!, whose first argument is a model, and the second argument is a selection strategy. The third and fourth positional arguments are, respectively, a list of variables to be included, and the folds to use for cross-validation. They can be omitted and would default to no default variables, and k-fold cross-validation.

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SDeMo.ForwardSelection Type

ForwardSelection

Variables are included one at a time until the performance of the models stops improving.

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SDeMo.BackwardSelection Type

ForwardSelection

Variables are removed one at a time until the performance of the models stops improving.

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SDeMo.AllVariables Type

AllVariables

All variables in the training dataset are used. Note that this also crossvalidates and trains the model.

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SDeMo.VarianceInflationFactor Type

VarianceInflationFactor{N}

Removes variables one at a time until the largest VIF is lower than N (a floating point number), or the performancde of the model stops increasing. Note that the resulting set of variables may have a largest VIF larger than the threshold. See StrictVarianceInflationFactor for an alternative.

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SDeMo.StrictVarianceInflationFactor Type

StrictVarianceInflationFactor{N}

Removes variables one at a time until the largest VIF is lower than N (a floating point number). By contrast with VarianceInflationFactor, this approach to variable selection will not cross-validate the model, and might result in a model that is far worse than any other variable selection technique.

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Feature importance

SDeMo.variableimportance Function

variableimportance(model, folds, variable; reps=10, optimality=mcc, kwargs...)

Returns the importance of one variable in the model. The samples keyword fixes the number of bootstraps to run (defaults to 10, which is not enough!).

The keywords are passed to ConfusionMatrix.

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variableimportance(model, folds; kwargs...)

Returns the importance of all variables in the model. The keywords are passed to variableimportance.

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Variance Inflation Factor

SDeMo.stepwisevif! Function

stepwisevif!(model::SDM, limit, tr=:;kwargs...)

Drops the variables with the largest variance inflation from the model, until all VIFs are under the threshold. The last positional argument (defaults to :) is the indices to use for the VIF calculation. All keyword arguments are passed to train!.

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SDeMo.vif Function

vif(::Matrix)

Returns the variance inflation factor for each variable in a matrix, as the diagonal of the inverse of the correlation matrix between predictors.

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vif(::AbstractSDM, tr=:)

Returns the VIF for the variables used in a SDM, optionally restricting to some training instances (defaults to : for all points). The VIF is calculated on the de-meaned predictors.

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