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Hello, I have a data set with ~80 records, with ~8 features. I want to predict one of the features in future records. The feature is numeric and discrete. It ranges between -30 up to 140 with steps of 5. This feature doesn't normally distributed Until now I wanted to predict another feature which is boolean, so I used logistic regression. Which method should I use here? Maybe some kind of particle filter? Thanks! |
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Have you considered matrix completion methods? The general idea is you've a matrix with some missing values (in your case features) and you want to impute these from the existing values. There are supervised and semi-supervised matrix completion methods that might suit your purpose. Check out the references section (and code) of this upcoming NIPS paper: http://www.cs.berkeley.edu/~ameet/dfc Thanks! I'll take a look
(Nov 27 '11 at 08:43)
Noam Peled
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If the feature you want to predict is numeric why not just use a linear regression and then round off the predictions to the closest discrete value?
Thanks! This is kind of what I'm doing, but I have one problem: I know my data is noisy, so I don't want to predict an exact value. I prefer a tool which will predict how the feature will distribute given the other features.
If the output variable is discrete, then what you went is classification not regression!
Indeed I can use classification, but I have more than 30 classes. Any thoughts?