slise.explain_find: 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 a binary search for sparsity!

Description

DEPRECATED: This is a simple binary search, no need for a separate function

Usage

slise.explain_find(
  ...,
  lambda1 = 5,
  variables = 4,
  iters = 10,
  treshold = 1e-04
)

Arguments

...	Arguments passed on to slise.explain 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 (default: NULL) 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)
lambda1	the starting value of the search
variables	number of non-zero coefficients
iters	number of search iterations
treshold	treshold for zero coefficient

Value

SLISE object

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