slise.explain_comb: SLISE Black Box Explainer Use SLISE for explaining...

In edahelsinki/slise: Sparse Linear Subset Explanations

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

Description

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

edahelsinki/slise documentation built on Aug. 24, 2023, 11:03 p.m.