SLISE Black Box Explainer Use SLISE for explaining predictions made by a black box. BUT with sparsity from a combinatorial search rather than Lasso!

SLISE Black Box Explainer Use SLISE for explaining predictions made by a black box. BUT with sparsity from a combinatorial search rather than Lasso!

Usage

slise.explain_comb(X, Y, epsilon, x, y = NULL, ..., variables = 4)

Arguments

X

matrix of independent variables

Y

vector of the dependent variable

epsilon

error tolerance

x

the sample to be explained (or index if y is null)

y

the prediction to be explained

...

Arguments passed on to slise.explain

lambda1

L1 regularisation coefficient (default: 0)

lambda2

L2 regularisation coefficient (default: 0)

weight

Optional weight vector (default: NULL)

normalise

Preprocess X and Y by scaling, note that epsilon is not scaled (default: FALSE)

logit

Logit transform Y from probabilities to real values (default: FALSE)

initialisation

function that gives the initial alpha and beta, or a list containing the initial alpha and beta (default: slise_initialisation_candidates)

variables

the number of non-zero coefficients

Value

SLISE object