ML algorithm suggestion for databases that change a lot with time after model training

Question

I have a classification problem and I'm using a logistic regression (I tested it among other models and this one was the best). I look for information from game sites and test if a user has the potential to be a buyer of certain games.

The problem is that lately some sites from which I get this information (and also from where I got the information to train the model) change weekly and, with that, part of the database I use for prediction is "partially" different from the one used for training (with different information for each user, in this case). Since when these sites started to change, the model's predictive ability has dropped considerably.

To solve this, an alternative would be, of course, to retrain the model. It's something we're considering, although we'll have to do it with some frequency given the fact that the sites are changing every couple of weeks, considerably.

other solutions considered was the use of algorithms that could adapt to these changes and, with that, we could retrain the model less frequently.

Two options raised were neural networks to classify or try to adapt some genetic algorithm. However, I have read that genetic algorithms would be very expensive and are not a good option for classification problems, given the fact that they may not converge.

Does anyone have any suggestions for a modeling approach that we can test?

Answer 1

It sounds to me that while the data is changing every week, it is still in the same domain. That should make things easier. You need a neural network that generalises well. Faster RCNN with ResNet as a backbone can be used for classification. It is a state-of-the-art network that generalises very well. You could also try Yolov4 with Darknet as a backbone. It is also state-of-the-art. On benchmark sets, Faster RCNN has a higher mAP while Yolov4 is faster.