Feature ranking

jimineep

Hi all,

I apologise if this has come up before, I did a quick search but couldn't find anything specifically addressing my issue.

I have a dataset which consists of a number of variables: continuous, date, multinomial and binomial. The data label is binomial.

There are a number of examples and tutorials for running subset selection in order to find the most informative variables in the data. However, I would like to do something more simple to begin, merely rank the variables (i.e. rank the features by a given metric).

Is there an easy way to do this using an operator? I.e. to feed my dataset into a method, and get an ordered list of variables out? Of course, the added complication is that I have different types of variable (i.e. continuous vs. categorical), but I suppose ranking by p.value would allow me to fuse the outputs.

Thanks in advance for any help you can give

steffen

Hello jimineep and welcome to rapidminer

The approach you are looking for is called "Filter" in the area of feature subset selection. Rapidminer provides a good amount of operators for this. See lefthandside Modelling -> Attribute Weighting.

greetings,

steffen