Discussion of article "Advanced resampling and selection of CatBoost models by brute-force method" - page 7
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In that notebook, only this code block gives an error
pr = get_prices(look_back=LOOK_BACK)
pr = add_labels(pr, 10, 25)
rep = tester(pr, MARKUP)
plt.plot(rep)
plt.show()
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
What can be the reason?
In that notebook, only this code block gives an error
pr = get_prices(look_back=LOOK_BACK)
pr = add_labels(pr, 10, 25)
rep = tester(pr, MARKUP)
plt.plot(rep)
plt.show()
ValueError: Cannot set a frame with no defined index and a value that cannot be converted to a Series
What can be the reason?
dataframe is empty
check if the quotes are received or not
You try it. It won't take long. Wouldn't it be interesting to test it in an experiment? Breiman didn't do it in his random forest.
It's slow. I'll try it later.
it's slow. I'll try it later.
dataframe empty
check whether quotes are received or not
that's right, I didn't pay attention to the fact that the broker has an "m" at the end of the eurobucks pair - EURUSDm.
It will be interesting to see the result. I think we can split the test in half, half for the test and half for the exam. Or add a couple of years.
I've done something like that before, a glass of wood. Actually, it didn't do anything great.
I doubt it in this case, too. But I'll check later.
I've done something like this before, a glass of wood. As a matter of fact, it did not give anything wonderful.
I doubt it in this case, too. But I'll check it later.
I agree, in the forest initially averaging the best results. But it doesn't hurt to check)
I agree, in the forest initially averaging the best results. But it doesn't hurt to check)
No, all of them.
And it's called a random forest because all random trees are summed.
For the best would not be called random forest, but best forest. )))
No, all of them.
And it's called a random forest because all the random trees add up.
For the best would be called the best forest, not random forest. )))
Apparently we have different ideas about random-boosting. Decisive tree, it's about selected features from a random set. The point is that the sets are random, but the selection / clustering into bad good ones was originally there. It's like throwing a needle, measuring angles and calculating Pi number)
from the wiki.
Apparently we have different ideas about random bousting. Decisive tree, it's about selected features from a random set. The point is that the sets are random, but the selection / clustering into bad good ones was originally there. It's like throwing a needle, measuring angles and calculating the number of pi)
from the wiki
Yes, there are many trees, but each one is trying to learn best on different features. This is not the same as combining multiple forests (including bad ones)