It's not semantics. Overfitting is a completely unrelated problem. Overfitting is an issue with the machine learning algorithm and how you're applying it and ca

It's not semantics. Overfitting is a completely unrelated problem. Overfitting is an issue with the machine learning algorithm and how you're applying it and can be fixed by changing how you use the algorithm, while what we're taking about here is a problem with the training data. There is nothing that can fix a biased training data set short of getting new training data that doesn't contain that bias. It would be like if your car was driving in circles and you called a mechanic to fix your steering, and they told you that the actual problem was that both right wheels were missing. That's not a steering problem, and no repair to the steering system will fix it. The only fix is to put new wheels on.