In this package you'll find a script implementing feature selection
using a version of the Boruta
algorithm
to detect important and unimportant fields in your dataset. The
algorithm:
Retrieves the dataset information.
Creates a new extended dataset. In the new dataset, each field has a
corresponding shadow field which has the same type but contains a random
sample of the values contained in the original one.
Creates a random forest from the extended dataset
Extracts the maximum of the importances for the shadow fields
Uses this maximum plus (minus) a minimum gain as threshold. Any of the
original fields scoring less than the minimal threshold are considered
unimportant and fields scoring more than the maximum threshold are
considered important.
Fields marked as unimportant are removed from the list of fields to be used
as input fields for new datasets
The procedure is repeated, and a new extended dataset is created with
the remaining fields. The process stops when it reaches the user-given number
of runs or when all the original fields in the dataset are marked as
important or unimportant.
When iteration stops, a new dataset is created where unimportant fields have
been removed.
