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There are no layers there, it's a tree booster
Man, the function is not linear and complex between the source and the forecast. But initially the goal is to match and correct forecast. that's why the correlation is usually positive. Not linear. And it makes no difference what the prediction algorithm is. The data is prepared for the purpose of the correct prediction. But all this is not yet enough for a reliable forecast.))))
Man, the function is not linear and complex between the source and the forecast. But the initial goal is to match and correct prediction. so usually the correlation is positive. Not linear. And it makes no difference what the prediction algorithm is. The data is prepared for the purpose of the correct prediction. But all this is not yet enough for a reliable forecast. ))))
F-square simple linear, p-square error
Well we are talking about different things. p-squared errors is an estimate of the forecast. not the dependence - a function of the raw data to the forecast. in the estimate is simple, the data are real and forecast, but the dependence on the raw forecast data is complicated. And it usually isn't made. It's complicated. It's easier to estimate the result.
Well, we're talking about different things. p-squared error is an estimate of the forecast. not the dependence - a function of the raw data to the forecast. in the estimate is simple, the data are real and forecast, but the dependence on the raw data forecast is complex. And it usually isn't made. It's complicated. It's easier to estimate the result.
Well he asked for the result, whether the model shows dependence or not. The classic one does not. Maybe some kind of sophisticated model will.
Man, I'm getting a little uncomfortable... I didn't have to lecture you.)))) Let me ask you a question. Correctly understood the python tutorial. The global locality of names is determined by the place of the name declaration, but the actions with them if in someone else's namespace are determined by global locale prefixes. and if without prefixes, the namespace name is prioritized. Interesting namespace logic, especially after BASIC))))
on five previous values:
60% accuracy
on 10 preceding deteriorates, accuracy 50
accuracy 57 on the 3rd.
Blue initial, orange prediction. The last 300 values.
Thanks Maxim!
Actually, I forgot to say, it is enough that the model predicts the next sign (+-) of the series, target + plus value. And the specific accuracy is not important.
A sample of length n = 54 , covers all possible values.
Man, I already feel a little uncomfortable... I was kind of not lecturing)))) Let me ask you a question. Correctly understood the python tutorial. The global locality of names is determined by the place of the name declaration, but the actions with them if in someone else's namespace are determined by global locale prefixes. and if without prefixes, the namespace name is prioritized. Interesting namespace logic, especially after BASIC))))
Thank you Maxim!
Actually, I forgot to say, it is enough that the model predicted the next sign (+-) of the series, target + plus value. And the specific accuracy is not important.
The sample length n = 54 , covers all possible values.
I will do it later today, if I have time.
Yes.
Thanks. It's worth something. The explicit freedom in variable types turns out to be an explicit type indication. Although if there are no identical names, it should work without prefixes.
cps. everything is worth something. explicit freedom in variable types turns out to be an explicit type indication. Although if there are no identical names, it should work without prefixes.