Machine learning in trading: theory, models, practice and algo-trading - page 3524
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Separate file for each cluster
Thanks.
I took one sample (cluster), trained 100 models with different seed, 10 trees of depth 6, tempo 0.03.
This is the spread in accuracy - not very critical, but significant.
In terms of responses, it is much more significant.
And, I did not change any other settings - which are many in CatBoost.
As a result I consider that one model is not enough to estimate quality of markup with the help of a model.
Thank you.
Took one sample (cluster), trained 100 models with different seed, 10 trees each with depth 6, tempo 0.03.
This is the variation in accuracy - not very critical, but significant.
In terms of responses, it is much more significant.
And, I did not change any other settings - which are many in CatBoost.
As a result, I think that one model is not enough to evaluate the quality of markup with the help of a model.
Well, that's the average.
Ah, it's on a different principle than the cv articles. Nothing was filtered here.Well, that's how the average is rated
I understood that one (way two) models are taken for each markup - am I misunderstanding?
But for the balance of the error - there are such a variety of totals.
Worst variant - balance and model
The best variant - balance and model.
All on one sample - what will be there on future data - I do not know. The main thing is that there is diversity.
ah, it's on a different principle than the cv articles. There was no filtering.
You just randomly scattered grains?
Just randomly scattered grains(units)?
With a random number of forward prediction bars. Then which way the profit is so marked, 0 or 1.
So forget it, you will not find anything in these datasets :) they work in conjunction with the second model, 2 models at once.
It's a real pleasure, a real improvement. I touched upon the entropy topic for a reason.
Well, secrets of change I do not ask, only I will note, that in CatBoost since some version there made automatic balancing of metrics on proportion of classes - if you constantly change markup, for you it should be critical - I recommend to switch off balancing (use of weights).
With a random number of forward prediction bars. Then in which direction the profit is marked, 0 or 1.
So forget it, you will not find anything in these datasets :) they work in conjunction with the second model, 2 models at once.
Yeah, he probably won't find anything that way.
Well, secrets of change I do not ask, only I will note, that in CatBoost since some version there made automatic balancing of metrics on proportion of classes - if you constantly change markup, then for you it should be critical - I recommend to switch off balancing (use of weights).
It's been working for a long time. It's finding it. And you're still teaching mashka, it's been years....