1
$\begingroup$

I am using logistic regression to train a model to predict 'click/non-click' using ['browser info', 'publisher info', , 'location', 'time', 'day'].

I wanted to know the ways in which I can use the new live data to improve the improve the already trained model.

Does a solution exist which takes into account - change in feature set?

$\endgroup$
2
$\begingroup$

Suppose you have a model that has been trained on $N$ data over $E$ epochs. This means that the model has seen each of the $N$ examples, $E$ times.

Now say you got $M$ more training data. Normally you would want to train the new ones for $E$ epochs as well.

However if $N$ and $M$ don't come from the same underlying distribution (or don't represent it adequately), this would result in the model "forgetting" the first $N$ examples and "paying more attention" to the latter $M$ ones.

You could try training your model for $<E$ epochs, so that it learns the latter but doesn't forget the former, but that is purely empirical and very hard to achieve in practice.

You can a few things to avoid this:

  1. Retrain your whole model using both the $N+M$ examples (which you would shuffle). This would require a new complete training of the model on regular occasions and would be increasingly difficult to train (due to the ever increasing size of the training data). This is a very inefficient solution and wouldn't work for any on-line training application
  2. Make use of a model that supports on-line training. Some algorithms support incremental (on-line) training, without you needing to retrain the whole thing. A scikit-learn comparison is available.
  3. Customize an algorithm so that is has the desired effect. For example, you could train a linear SVM incrementally, with a large regularization penalty and an SGD classifier. This is discussed in more detail for scikit-learn here.
| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.