Predict an unbalanced class

mfulgeri

Hi guys, 

I'm pretty new with RM so apologize if I'm asking someting straightfoward.

I'm trying to train a predictor on a class that is heavely unbalanced - say churn rate(on average I have 1 observation with chur every 999 observation without).

I read that boosted trees could be a good choice as algorthm, but I have some difficulties to understand if there is a way to have a "penalized" version of this.. how can I achieve this? 

Another approach that I could follow would be to generate new data based on the distribution of the other variables in the data set, is that possible to do in RM?

 

many thanks for your help! 

Matteo 

sgenzer

Hello @mfulgeri - welcome to the community.  Good question.  So I assume first of all that you've seen this video on churn prevention?  Second I would investigate the use of the Sample operator, using the "balance data" checkbox advanced parameter:

 

 

[EDIT: forgot to mention that I always use mod.rapidminer.com as a resource when trying to decide which learner would be most appropriate.  It's a very under-utlilized and amazing resource.]

 

Scott

Telcontar120

There was an almost identical question asked a few months ago, and rather than repeating my earlier somewhat detailed answer, I encourage you to check it out.
https://community.rapidminer.com/t5/RapidMiner-Studio-Forum/Dealing-with-imbalanced-data/m-p/41385
In short there are many options for dealing with class imbalance in RapidMiner.

sgenzer

thank you @Telcontar120 - I was looking for that post but could not find it!

Scott