This is a tiny amount of data. The regularization in Mahout's SGD implementation is probably not as effective as second order techniques for such tiny data.
Btw... you didn't answer my questions about what kind of data feature A and B are. I understand that you might be shy about this, but without that kind of information, I can't help you. (and add this additional question) What is the size of the encoded vector? On Mon, Jul 11, 2011 at 2:26 PM, Weihua Zhu <[email protected]> wrote: > Target class is if a user click an ad(advertisement), buy through an ad, or > not; so 3 classes. > Feature A s about the Advertisement itself; > Feature B is about the user's behaviors; > Currently im only using feature A and B. > Total training data is 250 for each class; > > thanks.. > > > ________________________________________ > From: Ted Dunning [[email protected]] > Sent: Monday, July 11, 2011 2:15 PM > To: [email protected] > Subject: Re: combination of features worsen the performance > > Can you say a little bit about the data? > > What are features A and B? What kind of data do they represent? > > How many other features are there? > > What is the target variable? How many possible values does it have? > > How much training data do you have? > > What sort of training are you doing? > > > > On Mon, Jul 11, 2011 at 2:08 PM, Weihua Zhu <[email protected]> wrote: > > > Hi, Dear all, > > > > I am using mahout logistic regression for classification; interestingly, > > for feature A, B, individually each has satisfactory performances, say > 65%, > > 80%, but when i combine them together(using encoder), the performance is > > like 72%. Shouldn't the performance be better? Any thoughts? Thanks a > lot, > > > > > > -wz. > > >
