Machine learning in trading: theory, models, practice and algo-trading - page 3385
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I don't know, I don't really have anything to write.
Well, in trees you can usually calculate the influence of each observation of each trait, its contribution to the model, for example, through shap values. If you leave only useful ones and train something only on them, you will get an approximate analogue of rule search. With neurons, by the way, it is also possible.
The impact of each feature, the impact of each observation, and the impact of each rule are all different
Rules are the elements of the model that link attributes and labels. The only thing is that neural networks do not have discontinuity, but it can be artificially made.
I'll try from Khabarovsk...
any model is a certain sum of patterns, exaggeratedly a pattern can be labelled as a TS.
Let's imagine that a model consists of 100 TCs.
It can be that in model #1 100 TSs made one deal.
It can be that in pattern #2 one TS made 100 trades, and the other 99 did not make a single trade.
how to calculate statistics for each TS?
If the model is a rule model, it is easy and clear.
If the model isneural?
I'll try from near Khabarovsk.
If the model is neuron?
Well, we got a subsample where neuronics predicts well. How do you know if it's one pattern in that subsample, two or twenty? You really don't know the difference?
By the number of examples left. There are as many examples as there are patterns.
There can be 200 examples and only 5 patterns.
I'll try from Khabarovsk...
Any model is a certain sum of patterns, exaggeratedly, a pattern can be labelled as a TS.
Let's imagine that a model consists of 100 TS.
It can be that in model #1 100 TCs made one deal.
It can be that in model #2 one TS made 100 deals, and the other 99 did not make any deals.
how to calculate the statistics for each TS?
If the model is from the rules, it can be done easily and clearly.
If the model isneural?
The problem is not the number of times the model is used.
The problem is that the same model (tree?) on the same data predicts one label in some cases and a different label in other cases. This is what is called classification error. There are no predictors, at least with us, whose values can be categorised strictly into classes. and all the problems with leaves, trees and whatnot are derived from the values of the predictors.