[YeGoblynQueenne]

>> Sure, and we'd all love to be using those. But even if you generalize well from small datasets, you still generalize better from larger ones.

Not to my knowledge. What techniques did you have in mind that work like that?

[darawk]

Literally all of them? Linear regression, neural networks, KNN, I could just enumerate all ML methods here, but I think the foregoing is sufficient.

[YeGoblynQueenne]

I'm sorry, I don't understand. Which of the above generalises well from small datasets?

[darawk]

Who said they do? I said they generalize better from larger datasets. The entire point of this discussion is that more data is better.

[YeGoblynQueenne]

I was referring to this part of our exchange:

ME: There are machine learning techniques that generalise well from few data, but they are not very well known in the industry.

YOU: Sure, and we'd all love to be using those. But even if you generalize well from small datasets, you still generalize better from larger ones.

That is not how those techniques work to my knowledge, so I was asking which ones you had in mind.

[darawk]

My point is that they all generalize better from larger datasets. Size is relative and some techniques work better with more or less data. Linear regression, for instance, can work quite well with much less data than a neural net. It just depends on the complexity of the problem.

[YeGoblynQueenne]

>> My point is that they all generalize better from larger datasets.

Like I say, this is not the case. There are learning algorithms that generalise so well from few data that their performance can improve only marginally with increasing amounts of data, or not at all.

I appreciate that you probably have no idea what I'm talking about. I certainly don't mean linear regression.