Reject Inferencing

reports01

I have just started using (read: discovering) Rapid-i... and using an old dataset to learn things.

One of the things I would like to do is: Reject inferencing

Let me explain what I feel it should do:
As I have a dataset of which only a certain percentage has a know outcome (say: 'good' and 'bad'), there is a big group of which I do not know the outcome (say: 'rejects' in credit risk or 'indeterminates' in any other project).

As I do want to incorporate this last group in my modelling (so my datasample has a complete view of the dataset), I would have to use a technique called: "reject inferencing", as by doing so I get a more trustworthy model that is based on my entire customer base.

But as I am using these cases to model on, I want a probability weighing on these cases of there likely behaviour, based on the variables used. Thus creating a training set that is representive of the entire population.
This is something that should be done in the premodeling stage.

So giving the known cases (good and bad) a weight of 100%
and unknown cases a likelyhood weight of good behaviour between 0 and 100%


Now my question:

How do I do this in rapid-i

steffen

Hello and welcome to RapidMiner

I must admit that I am really interested in this problem. I am currently dealing with the correction of the SampleSelectionBias which can be viewed as the rejection inference problem as well (according to this source: http://people.cs.uu.nl/ad/mbrejinf.pdf). Hence I am a little confused by your explanation...

reports01 wrote:

As I have a dataset of which only a certain percentage has a know outcome (say: 'good' and 'bad'), there is a big group of which I do not know the outcome (say: 'rejects' in credit risk or 'indeterminates' in any other project). As I do want to incorporate this last group in my modelling (so my datasample has a complete view of the dataset), I would have to use a technique called: "reject inferencing", as by doing so I get a more trustworthy model that is based on my entire customer base.

Ok

reports01 wrote:
But as I am using these cases to model on, I want a probability weighing on these cases of there likely behaviour, based on the variables used.

including outcome, yes or no ?

Thus creating a training set that is representive of the entire population. This is something that should be done in the premodeling stage.

This is the nontrivial task of the correction of the sampling selection bias (rejection inference problem). Adjusting the distribution of the trainingset to the distribution of basic population to get more reliable results !
I dont know if i got you right, but: Did you plan to infer the outcome where it is not known to finally learn a model based on both groups (which have both a known outcome now) ?

So giving the known cases (good and bad) a weight of 100% and unknown cases a likelyhood weight of good behaviour between 0 and 100%

Sorry, this makes no sense for me. Maybe you have confused the mechanism of whether the outcome is known with the mechanism of the outcome itself ?

How do I do this in rapid-i

Please be more specific with your task !

Summary: Maybe you are willing to tell which techniques you want to use to deal with the rejection inference ? This way help in how-to-use-RapidMiner can be provided more easily...

greetings

Steffen