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Good - the important thing is to get started! :)
MO for context, mashka for trading )))
Trading is all about risk/profit, not about akurasi... IMHO
a, MO is just a tool to describe your understanding of the market, your model ...
If there is no model and you think that MO is AI and it is all by itself, then you are in trouble for a long time....
Trading is all about risk/profit, not akurasi... IMHO
a, MO is just a tool to describe your understanding of the market, your model....
If there is no model and you think that MO is AI and it is all by itself, then you are in trouble for a long time....
The yellow one is adaptive? It's in the centre, it's horizontal.
The yellow one is adaptive? It's the one in the centre that goes horizontal.
No, it's just a normal one.
That's 100 years.
I haven't found any ready-made solutions, I have to parse the tree structure. It will take a bit longer.
Yes, it's the same for me in R - it saves the tree structure in its own weird way. Then I have a separate parser to pull out leaves.
And what library do you use that you immediately get inequalities as leaf rules?
Yes, that's how I have it in R too - it saves the tree structure in its own weird way. Then I have a separate parser to pull out leaves.
Something like this, you can sort by maximum importance in the model, by probability of belonging to a class and by frequency of use.
That's enough for today.
Something like this would be, you can sort by maximum importance in the model, by probability of class membership and by frequency of use
That's enough for today.
That's effective!
Do you code predictors into numbers from the master sample?
It's effective!
Do you code predictors into numbers from the master sample?
any names