Discussion of article "Advanced resampling and selection of CatBoost models by brute-force method" - page 9
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Here's an option for a nice improvement
Here's the dataset after resampling.
Train everywhere 5 months, validation 2 years, examination 5 years
all charts for 5 years
One more time.
Yeah, it makes sense sometimes.
A little more staking. Yeah, it makes sense to stack. It's still an open question as to how much.
Once again.
Well, yeah, it makes sense sometimes.
A little more staking. Yeah, it makes sense to stack. It's still an open question as to how much.
Here we go. )
That's why Breiman made a random forest, not a better one.
Here we go. )
That's why Bryman made a random forest instead of a better one.
Curiously, the effect appears when using Accuracy in training
It's not so obvious with other metrics.
by the way, there are a lot of them in catbusta, and I hesitate to ask which one is better to chooseCuriously, the effect appears when Accuracy is used in training
with other metrics, it's not so obvious.
By the way, there are a lot of them in catbusta, and I hesitate to ask which is the best one to chooseIf Accuracy improves balance the best, apparently it is the best.
If Accuracy improves balance the best, apparently it is the best.
No, there are some that do a better job.
but there's no meaningful understanding.
No, there are some that work better.
Which ones?