Machine learning in trading: theory, models, practice and algo-trading - page 3650

 
Evgeniy Chernish #:
I think people confuse the generalisation ability of NS with extrapolation. Extrapolation implies going beyond the boundaries of the training set and as a consequence either poor results or no results at all (depends on the type of NS). Generalisation implies good performance of the network on new data, but which do not go beyond the boundaries of the training set.
Therefore, networks approximate the data on the training sample and interpolate on new data that do not go beyond the training dataset.
Exactly
 
Accordingly, the very idea of extrapolating NS functions is meaningless.
 
Evgeniy Chernish #:
I think people confuse the generalisation ability of NS with extrapolation. Extrapolation implies going beyond the boundaries of the training set and as a consequence either poor results or no results at all (depends on the type of NS). Generalisation implies good performance of the network on new data, but which do not go beyond the boundaries of the training set.
Therefore, networks approximate the data on the training sample and interpolate on new data that do not go beyond the training dataset.
Maxim Dmitrievsky #:
Exactly
Maxim Dmitrievsky #:
Accordingly, the very idea of extrapolation of NS functions is meaningless.


Wait, then how to teach NS to calculator?

 
Forester #:

I gave Dick a link to a simple tree training code. If he had spent 20 minutes to study it, he would not have written here for 4 days his fantasies about extrapolation to MO.
Well, he obviously came here to troll....
But it turns out that the regulars of this thread haven't seen the code either.

I'll explain in a very simple way. There was an example of teaching a forest the multiplication table from 1 to 9. Using his example:

If you trained up to 9*9 at most, the corresponding leaf will always answer 81. And at queries 10*9 and 9*10 and 11*20 and even 1000*2000 the answer will always be 81. There are no leaves with answers 90,100,220, 2000000.
What extrapolation? The answers will only be from 1 to 81.

That's some rubbish you've written, Forester.

I don't need your links if I've written these algorithms myself, I can't count how many.))

You do not understand some things, fundamental, do not understand the scope of applicability of MO methods. I can't help you, it's too much to explain, it's expensive.

 
Maxim Dmitrievsky #:
Well, try extrapolating a series of natural numbers with a neural net ) boosts are even worse at it.

It's easier just with your finger on the asphalt.

 
Ivan Butko #:


Wait, then how do you teach NS to calculate?

On the entire set of example-answers. You can thin them out and the NS interpolates the missing examples with a given precision.
 
Andrey Dik #:

It's easier just to put your finger on the pavement.

Apart from blabbering and tit-making, what else is there?)
 
Maxim Dmitrievsky #:
Will there be anything other than blathering and titty-making?)

Yeah, there will be.

Let's make a deal. I will show you the open source code here, not in some python, but the whole source code in MQL5. After that, if I solve the problem, you will never cross my path again. True, in this case you will be a blabbermouth. But it's still a good deal for you, because otherwise it turns out that you can't solve the simplest task.

Are you going to refuse the deal or accept it?

 
Andrey Dik #:

Yes, it will.

Let's make a deal. I will show you the open source code here, not in some python, but the entire source code in MQL5. After that, if I solve the problem, you will never cross my path again. True, in this case you will be a blabbermouth. But it is still a good deal for you, because otherwise it turns out that you cannot solve the simplest task.

Are you going to refuse the deal or accept it?

A deal with a blabbermouth? I'd rather make a deal with the devil. I'm fine with it for now.
We still have to prove the deal.
 
To get out of the situation, you're going to have to do a lot of nagging, brainwashing. But you won't escape the obvious.