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I'd argue that the case you give isn't an example of using a computer to generate data, it's a case of a human adding data (the data being the fact that the bin
by kerkeslager 2y ago
I'd argue that the case you give isn't an example of using a computer to generate data, it's a case of a human adding data (the data being the fact that the binary classifier should have a 50/50 balance).
This sort of massaging of data has its drawbacks as well--obviously this only works if the balance of that binary classifier actually is 50/50 in reality: I don't know enough about your case to say you were wrong, but I can imagine a lot of scenarios where a binary classifier should not be represented 50/50 in the data.
- axegon_ 2y agoThis is a question of definition. It is synthetic in that I just passed a prompt, asking for N amount of examples of X. And I did not go over the entire list I got and blindly trusted it. In this context, I needed an even(or nearly even distribution) of samples in the training data and it worked way better than I was hoping. Mind you, I have to face a similar issue next week and I'm not sure this approach would cut it - I need way more training data and way more classes to work with. 249 classes if I'm not mistaking.
- kerkeslager 2y agoI question what "it worked way better than I was hoping" means in this context. If you're saying that filtering the input data to create a uniform distribution created a uniform distribution in the output, I'm not sure why you'd hope for any less--that's exactly what I'd expect to happen. But that's a poor measure of success, because you don't know what side effects that had: the removed data ostensibly contained other variables besides your binary variable, and you don't know if those variables were sampled in any useful way, so I'd be hesitant to say this worked well without at least an attempt to measure those other variables.