Newsgroups: comp.ai.neural-nets
Path: cantaloupe.srv.cs.cmu.edu!rochester!udel!gatech!howland.reston.ans.net!pipex!warwick!yama.mcc.ac.uk!cf-cm!C.M.Sully
From: C.M.Sully@cm.cf.ac.uk (Chris Sully)
Subject: bias
Message-ID: <1995Apr28.195623.7533@cm.cf.ac.uk>
Sender: C.M.Sully@cm.cf.ac.uk (Chris Sully)
Nntp-Posting-Host: aquamarine.cm.cf.ac.uk
Organization: University of Wales College of Cardiff, Cardiff, WALES, UK.
Date: Fri, 28 Apr 1995 19:56:22 +0000
Lines: 36

Let's see if I can get this right ...

I've trained a backpropagation ANN to predict the birthweight of babies given
11 predictor variables. I started with 8400ish records in the training set but
performance was approx. as good with 1000 so I reduced to this, using a validation set of 500 records to determine optimum performance on unseen data and a test set of 500 records to judge performance.

The statisticians who provided the data preferred to see the results in terms
of residuals (actual outputs-predicted outputs). They expected that the 
mean of the residuals would be zero. It wasn't, being around 10-20 grammes
(typical birthweights being around 4000 grammes if I recall correctly).

It seems there is a degree of bias in the model, with the model consisting
of two parts, the data and the neural network.

If this is a bias introduced by the network would someone explain how this has
come about/ introduce me to a few appropriate references/ suggest pathways
of further investigation.

Comments on the above would also be most welcome.

Thanks in adavnce.

Chris.

========================================================
Christopher Sully
Ph.D. Student (Neural Networks)  
Department of Computing Mathematics (Room M1.36), 
University of Wales College of Cardiff,   
PO Box 916, CARDIFF CF2 4YN, Wales, UK.   
E-Mail: C.M.Sully@cm.cf.ac.uk
WWW: http://www.cm.cf.ac.uk/User/C.M.Sully
Phone:  +44 (0)222 874000 x6070
Fax:	+44 (0)222 666182
Home:	+44 (0)222 484494
=========================================================
