Machine learning in trading: theory, models, practice and algo-trading - page 494
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Read Heikin Neural Networks and Bishop's theory in English - no translation, but it looks like it's being prepared.
Everything is simple. Random trades for input, and results for output. The Monte Carlo method is called, and it is not very fast per se. And the systematization is a business of the National System.
Well, is there a special name for the NS? Like a stochastic annealing neural network of unclear learning with or without a teacher, and optimizing inputs instead of outputs :))) I'll read books,
There's a book by Haykin "NS Complete Course, Second Edition" in Russian.
Well, is there a special name for NS itself? Like a stochastic annealing neural network of unclear learning with or without a teacher, and optimizing inputs instead of outputs :))) I'll read books,
Heikin "NS Complete Course Second Edition" is available in Russian
Heikin is, Bishop is not available in Russian.
NS is the usual MLP, training is the usual BP, only with regular manual readjustments as you go along. If these readjustments are not done, or just shuffle the sample, it learns very quickly, but works well (even perfectly)) only on the learning sequence.
Heikin is there, no Bishop in Russian.
The NS is the usual MLP, the training is the usual BP, only with regular manual readjustments as the play progresses. If you don't make such readjustments or just shuffle the sample, it learns very fast, but works well (even perfectly)) only on the learning sequence.
For now I'll make do with Haykin's work. Always limited myself to articles and model descriptions, books are very superfluous (so that there is enough material to sell).
And Haykin's is old:) so far I'll do without it, I always limited myself to articles and descriptions of models, the books have a lot of unnecessary (that would be a volume for sale)
False statement. Normal and boosted forests do not differ from NS in extrapolation.
Every article I come across says the same thing
https://habrahabr.ru/company/ods/blog/322534/
http://alglib.sources.ru/dataanalysis/decisionforest.php
Extrapolation is tough for trees! - Peter's stats stuff
http://ellisp.github.io/blog/2016/12/10/extrapolation
Random Forest unable to predict outside of training data
https://www.quantopian.com/posts/random-forest-unable-to-predict-outside-of-training-data
Random forest regression not predicting higher than training data
Every article I come across says the same thing
https://habrahabr.ru/company/ods/blog/322534/
http://alglib.sources.ru/dataanalysis/decisionforest.php
Extrapolation is tough for trees! - Peter's stats stuff
http://ellisp.github.io/blog/2016/12/10/extrapolation
Random Forest unable to predict outside of training data
https://www.quantopian.com/posts/random-forest-unable-to-predict-outside-of-training-data
Random forest regression not predicting higher than training data
Bullshit is written by uneducated people. They haven't heard about retraining, have no idea about datamining, haven't heard about noise predictors, and don't know how to estimate models. They are just a kind of big-aged snobs playing intellectual games.
Bullshit is written by uneducated people. They haven't heard of retraining, have no idea about datamining, haven't heard of noise predictors, and don't know how to estimate models. It's just the kind of overgrown snobs who play mind games.
What does all this have to do with extrapolation...
those who wrote the RF in the alglib library are also uneducated people?
and r bloggers are clueless too, apparently
https://www.r-bloggers.com/extrapolation-is-tough-for-trees/
everyone is a loser, except the FA
only the FA's are accounted for.
;))
everyone is a loser, except the FA
only the FA's are accounted for.
;))
That's how people use RF without understanding the principles, and then they say it doesn't work... it's obvious from the last article that RF can't extrapolate, so it should only work on familiar data
Alas, but they are mistaken and it is normal not only for "ignoramuses" and snobs, remember Minsky and his authoritative opinion regarding "futility" of multilayer perseptrons)))
I'm not even speaking about articles on hubra, it's the same as throw-ups on forums, 99.9% of nuchpop advertising and 0.1% of true trash, 0.1% of sensible thoughts "between the lines".the man gave an example on R, in what place he made a mistake? unfortunately I don't use R, but I can even reproduce it myself