Imagine I'm collecting some training data. Lets say I collect a 30minute time series from 1000 people so I have 1000 observations (rows) in my feature matrix. I train some model (lets say a neural net for this example) and I find my AUC is really poor and I believe the problem to be the fact that I only have 1000 observations - so I don't have enough data
However, I am now unable to collect anymore data. One thing I could do, is take that 30minute time series, and slice it up into 30 1-minute sections. Then use these 1 minute series as rows in my data. So I would end up with 30 observations per person, and 1000 people, giving me 30000 rows in my feature matrix. I've now increased the size of my training set by 30x
If I were to do this when doing statistical/inferential tests I would be violating the assumption of independence, and I'd be forced to model this with some multilevel model to correct
Is this the same as the IID assumption in machine learning, where each of your rows must be independent of each other?
For inferential tests, (one of) the reason why this assumption matters is because it affects the Type 1 error of your inference. However, in machine learning we're not doing any inferential tests, so what effect does violating IID actually have on results? In other words, why do rows need to be independent of each other? Especially in a case like above where I can drastically increase the size of my training set by reusing different parts of 1 person's data