default_fs_fun: Default (example) feature selection function for RAFS

In RAFS: Robust Aggregative Feature Selection

Default (example) feature selection function for RAFS

Description

See run_rafs for how it is used. Only the train portion of the dataset is to be fed into this function.

Usage

default_fs_fun(train_data, train_decision, seed)

Arguments

train_data	input data where columns are variables and rows are observations (all numeric)
train_decision	decision variable as a binary sequence of length equal to number of observations
seed	a numerical seed

Details

The function MUST use this train_data and MAY ignore the train_decision.

If the function depends on randomness, it MUST use the seed parameter to seed the PRNG.

The function needs to return a list with at least two elements: rel_vars and rel_vars_rank, which are vectors and contain, respectively, the indices of variables considered relevant and the rank for each relevant variable. The function MAY return a list with more elements.

Other examples of sensible functions are included in the tests of this package.

Value

A list with at least two fields: rel_vars and rel_vars_rank, which are vectors and contain, respectively, the indices of variables considered relevant and the rank for each relevant variable.

RAFS documentation built on April 3, 2025, 5:27 p.m.