Features selection and importance

Feature selection

SDeMo.noselection! Function

noselection!(model, folds; verbose::Bool = false, kwargs...)

Returns the model to the state where all variables are used.

All keyword arguments are passed to train!.

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SDeMo.backwardselection! Function

backwardselection!(model, folds; verbose::Bool = false, optimality=mcc, kwargs...)

Removes variables one at a time until the optimality measure stops increasing.

All keyword arguments are passed to crossvalidate!.

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SDeMo.forwardselection! Function

forwardselection!(model, folds, pool; verbose::Bool = false, optimality=mcc, kwargs...)

Adds variables one at a time until the optimality measure stops increasing. The variables in pool are added at the start.

All keyword arguments are passed to crossvalidate!.

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forwardselection!(model, folds; verbose::Bool = false, optimality=mcc, kwargs...)

Adds variables one at a time until the optimality measure stops increasing.

All keyword arguments are passed to crossvalidate!.

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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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