slise.preprocess: Preprocess the data as necessary before running SLISE

In edahelsinki/slise: Sparse Linear Subset Explanations

Preprocess the data as necessary before running SLISE

Description

Preprocess the data as necessary before running SLISE

Usage

slise.preprocess(
  X,
  Y,
  epsilon,
  x = NULL,
  y = NULL,
  lambda1 = 0,
  lambda2 = 0,
  weight = NULL,
  intercept = FALSE,
  normalise = FALSE,
  logit = FALSE
)

Arguments

X	Matrix of independent variables
Y	Vector of the target variable
epsilon	Error tolerance
x	The sample to be explained (or index if y is null)
y	The prediction to be explained (default: NULL)
lambda1	L1 regularisation coefficient (default: 0)
lambda2	L2 regularisation coefficient (default: 0)
weight	Optional weight vector (default: NULL)
intercept	Should an intercept be added (default: TRUE)
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)

Value

list(X_orig, Y_orig, X, Y, x_orig, y_orig, x, y)

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