Mailing-List: contact user-help@mahout.apache.org; run by ezmlm
Precedence: bulk
Reply-To: user@mahout.apache.org
Received-SPF: pass (athena.apache.org: local policy)
MIME-Version: 1.0
Date: Mon, 3 Feb 2014 21:33:42 +0100
Message-ID: 
 <CAJBgT0n7Aq4UB=F0nG8mzfM+p5arfd9p1uaPHDgq-0UjtKRF7Q@mail.gmail.com>
Subject: SGD classifier demo app
From: Frank Scholten <frank@frankscholten.nl>
To: user@mahout.apache.org
Content-Type: multipart/alternative; boundary=485b397dceab8273d204f18670d9

--485b397dceab8273d204f18670d9
Content-Type: text/plain; charset=ISO-8859-1

Hi all,

I am exploring Mahout's SGD classifier and like some feedback because I
think I didn't properly configure things.

I created an example app that trains an SGD classifier on the 'bank
marketing' dataset from UCI:
http://archive.ics.uci.edu/ml/datasets/Bank+Marketing

My app is at: https://github.com/frankscholten/mahout-sgd-bank-marketing

The app reads a CSV file of telephone calls, encodes the features into a
vector and tries to predict whether a customer answers yes to a business
proposal.

I do a few runs and measure accuracy but I'm I don't trust the results.
When I only use an intercept term as a feature I get around 88% accuracy
and when I add all features it drops to around 85%. Is this perhaps because
the dataset highly unbalanced? Most customers answer no. Or is the
classifier biased to predict 0 as the target code when it doesn't have any
data to go with?

Any other comments about my code or improvements I can make in the app are
welcome! :)

Cheers,

Frank

--485b397dceab8273d204f18670d9--