Explanations

Shapley values

SDeMo.explain Function

explain(model::AbstractSDM, j; observation = nothing, instances = nothing, samples = 100, kwargs..., )

Uses the MCMC approximation of Shapley values to provide explanations to specific predictions. The second argument j is the variable for which the explanation should be provided.

The observation keywords is a row in the instances dataset for which explanations must be provided. If instances is nothing, the explanations will be given on the training data.

All other keyword arguments are passed to predict.

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Counterfactuals

SDeMo.counterfactual Function

counterfactual(model::AbstractSDM, x::Vector{T}, yhat, λ; maxiter=100, minvar=5e-5, kwargs...) where {T <: Number}

Generates one counterfactual explanation given an input vector x, and a target rule to reach yhat. The learning rate is λ. The maximum number of iterations used in the Nelder-Mead algorithm is maxiter, and the variance improvement under which the model will stop is minvar. Other keywords are passed to predict.

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Partial responses

SDeMo.partialresponse Function

partialresponse(model::T, i::Integer, args...; inflated::Bool, kwargs...)

This method returns the partial response of applying the trained model to a simulated dataset where all variables except i are set to their mean value. The inflated keywork, when set to true, will instead pick a random value within the range of the observations.

The different arguments that can follow the variable position are

• nothing, where the unique values for the i-th variable are used (sorted)

• a number, in which point that many evenly spaced points within the range of the variable are used

• an array, in which case each value of this array is evaluated

All keyword arguments are passed to predict.

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partialresponse(model::T, i::Integer, j::Integer, s::Tuple=(50, 50); inflated::Bool, kwargs...)

This method returns the partial response of applying the trained model to a simulated dataset where all variables except i and j are set to their mean value.

This function will return a grid corresponding to evenly spaced values of i and j, the size of which is given by the last argument s (defaults to 50 × 50).

All keyword arguments are passed to predict.

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