Machine learning in trading: theory, models, practice and algo-trading - page 3145
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I understand that, you could also look into causal forest. By the way, I haven't studied it, if someone will figure it out, it would be interesting to read about experiments with it
No. My sko is the "predictive ability" deviations. Nothing to do with the estimation of the model itself
No. My sco is about deviations in "predictive ability". It has nothing to do with the evaluation of the model itself
In the course of a similar experiment of selecting informative features, I tried all the ways. It's not difficult. Starting from correlation, mutual information and knn, through OLS and SVM to forest, bousting and neural networks (I didn't touch deep ones). It turned out to be best through bousting. OLS is in second place.
No. My sco is about deviations in "predictive ability". Nothing to do with the evaluation of the model itself
Is it possible that the parameters of the model jump very much from step to step? That is, despite good "predictive power" at each step, the desired dependence is arranged very differently and is constantly changing. If so, this may well be a form of overtraining.
I understand that, you could also look into causal forest. By the way, I haven't studied it, but if someone understands it, it would be interesting to read about experiments with it
It seems to be the same random forest, but with a causal interpretation. So you, as a populariser among us of forests and now of causal forests, have the cards in your hands).
Still, I don't understand the application of causal for trading yet. A quick googling did not help to find direct applications, only indirect ones - like studying the influence of stocks on Forex.
It seems to be the same random forest, but with a causal interpretation. So you, as a populariser of forests and causal interpretation among us, have the cards in your hands).
Still, I don't understand the application of causal for trading yet. A quick googling did not help to find direct applications, only indirect ones - like studying the influence of stocks on Forex.
Is it possible that from step to step the parameters of the model jump very much? That is, despite good "predictability" at each step, the desired dependence is arranged very differently and is constantly changing. If so, this may well be a type of overtraining.
In my case it is impossible to answer your question: the model is being retrained at each step, and naturally the feature set may be different at different steps.
The classification error varies from 20% to 10%. 25% has never happened.
In the course of a similar experiment of selecting informative features, I tried all the ways. It's not difficult. Starting from correlation, mutual information and knn, through OLS and SVM to forest, bousting and neural networks (I didn't touch deep ones). It turned out to be best through bousting. OLS is in second place.
None of the above algorithms do NOT give predictive power, nor do hundreds of MO algorithms that stupidly calculate importance, which shows how often the algorithm uses a feature: If fed rubbish to an MO algorithm, any MO algorithm will compute the importance of that rubbish.
None of the above algorithms give predictive power, nor do hundreds of MO algorithms that stupidly calculate importance, which shows how often a feature is used by the algorithm: If you feed rubbish into an MO algorithm, any MO algorithm will calculate the importance of that rubbish.
Classification/regression error gives. I think enough of these weird games to play, going round in circles :) There's a door to get out.
We go round in circles in two because you are stupid, supposedly not understanding what I write.
There won't be a code. Think for yourself.