SLISE Black Box Explainer Use SLISE for explaining predictions made by a black box. BUT with a binary search for sparsity!

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 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

SLISE object