I have yet to choose a classifier to get my model but I wanted to see which possible classifiers fit what I'm looking for. In fact, I need an algorithm to establish offline training this way:
- The first training starts after a month of collecting data (I know it's a short time but I want to see how it performs and how it gets better with time and more data) => first model is made
- After another month, I've got data collected after two months in total, so I want to establish a new model since my training data has just doubled and the model should provide better results => second model
- this continues on and on for a year...
Question1: After two months, when I want to add the new training data, would the previous model be updated to make the new model or would the training start all over as if I don't have a previous model?
Question2: Do all classifiers behave the same when new training samples are introduced?
I don't know if this is relevant, but my problem is a low-dimensional one: about 5 inputs (user location, date, time...) to predict one output.