Analyzing survey data for predictions I've got survey data that resembles:

|-------------| Q1a | Q1b | Q1c | Q2a | Q2b | Q2c | Classification
| Respondent  | 1   | 0   | 0   | 1   | 0   | 0   | Red
| Respondent  | 0   | 0   | 1   | 1   | 0   | 0   | Green
| Respondent  | 0   | 1   | 0   | 0   | 0   | 1   | Yellow


I am trying to predict the classification for new respondents. Currently I'm using a Naive Bayes, and getting pretty bad accuracy (~20%). I don't have much training data, and the training data is hand scraped from non-standard sources (internal company procedures are a mess here).

I'm looking for other ways to predict the classification.

I'm thinking about assigning weights to each question, and magically predicting the result based on those, somehow. Although I don't really know where to start learning about how to do that, and whether it's appropriate for this data. I have very little background in this :(

Any ideas or tips on predicting the classification column with no training data?
Can you give a bit more information on the size of the data you're training on (and if it's really 6 parameters you're basing the predictions off of)?  If it's really 6 questions with binary answers (1, 0 as you suggest), then there are 2^6 (i.e. 64) unique answer combinations, and to determine a probability for them you'll want a multiple entries per combination.  Standard error scales like 1/sqrt(n) so for 10% accuracy you'll need roughly 6,400 inputs which given your description, sounds like more data than you may have.  You may want to invest time into automating data collection.

If on the other hand, you have a reasonably large data set and are hoping for some alternative models, both boosted decision trees and random forest models sound like good candidates for this problem.