Machine learning in trading: theory, models, practice and algo-trading - page 2503
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No, indeed
Exactly. NS is a section of mathematics which is used when there is no explicit solution to a problem using ordinary formulas, however complex, when the formula of solution itself is not known.
The division of mathematics into classical and NS is my subjective opinion, of course NS is a section of mathematics, but otherwise.... Here is an example.
I preprocessing of input data using mathematics, I calculate the basic strategy with ordinary mathematics, logic and algorithms BUT to find out whether the signal is true or false, then I use NS, because the formula that determines the truth or false I do not know. So, in a nutshell...
If you need to "partition the input dataset into classes", what prevents you from doing it with regular trees or ensembles of trees?
In general, I would like to expand on the topic of MO. Finding probability patterns is a correct task, but not complete. More complete is the interpretation of long or strong regularities to the real, the foundation.
to the foundation won't work, the foundation rules itself... the foundations of classical economics are models, but on their basis the regulator makes decisions - the one who will not continue the trend if/when from his/her professional point of view it ruins social well-being (+/-) and social-economic development... I do not get into the subject, having lived (as a witness in the market) a complete eco-cycle (and having seen the decisions made by the regulator) -- it is not up for discussion (but macro-economic textbooks have everything) -- we do not measure, they measure and make decisions themselves, the retail trader can only accept the given/factual situation, as it is
Do you know the formula for data partitioning beforehand?
Clustering Evaluation in Python- not a formula, but still... logic!
p.s.
for training models available in Spark ML by passing vector column and target variable.
to the foundation will not work, the foundation rules itself... The foundations of classical economics are models, but based on them the regulator makes decisions - the one who will not continue the trend if/when from his/her professional point of view it ruins social well-being (+/-) and social-economic development... I'm not getting into this topic, having lived (witnessed in the market) the full ec. cycle (and seen the decisions made by the regulator) -- it's not up for discussion (but macroeconomics textbooks have everything) -- we don't measure, they measure and make decisions themselves, retail trader can only accept the given/facts as it is
Why do I need macroeconomic data if my goal (like most) is 10-20 pipsov v day?
So answer your own question... I, for example, have other goals... that's why the market exists!
so you answer your own question... I, for example, have other goals... that's why there is a market!
What are your goals?
and you live/live according to your goals... don't fidget between your goals and the goals of others... and be happy... you can refine your plans on your own
There are tasks that are practically impossible to solve with elementary mathematical algorithms or logic, or the solution becomes extremely time-consuming and unreasonable. For example, it is necessary to divide the input dataset into classes. It is IMPOSSIBLE to do it with the help of mathematical formulas or any special algorithm, because the law by which these data is divided is not known and it is actually what you need to find then and use the Kohonen map or any other machine learning algorithm. There are problems with an implicit solution, such as the prediction of BP, which is not possible to do with the help of simple logic in mathematics, then they start to apply ANN. That is, neural networks are used where classical mathematics cannot give an answer or is powerless and the solution can only be found through training.
To solve such problems using elementary mathematics, you need to know the formula for the solution, which is what the NS actually does. Finds the solution to the problem by learning....
Can generate a test sample where there is not enough statistical data for a complete analysis - though it may be a bit flawed...
can generate a simulation model where lab is not possible...
probably can also integrate faster when the integral function depends on many factors and/or there are many measurements... - ...purely in terms of speed, faster than manual
probably something like this