Machine learning in trading: theory, models, practice and algo-trading - page 3505
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In NS quantisation is often used to reduce the size of layers, including by changing data types after quantisation. I didn't do it - that's why I didn't write about it.
In general I don't understand what you are leading to - I have already written my attitude and vision and agreed that it is possible to try clustering in my algorithm. For this purpose, I have been working with the clustering tree for two months ago - while the project is on pause.
Or what is this all about?
Because it often doesn't make much sense, but you describe everything so complicatedly that it's much harder to understand the description than it is to make sense of what's going on.
so you have to physically destroy the structure of your brain, reformat the connections. This leads to excessive consumption of food and alcohol.If we build a tree, all we have are leaves, and nothing but leaves) Well, okay, there are also branches. But they are made of leaves!)
You described a variant with leaf selection - I said that here leaves are not selected for further use from the resulting tree.
Because there's a lot of sense in it, but you describe it so complicatedly that it's harder to make sense of the description than it is to make sense of what's going on.
so you have to tear down the structure of your brain, reformat the connections.I heard your opinion of my work.
I have a different opinion. I hope to make a sample generator with fixed patterns soon - then we'll see which method is more effective in detecting predispositions.
The difficulty of perception is that I am doing something that is not written about, so to understand it you have to strain your brain - go into it, and you don't want to do that - especially if you have a preconceived notion.
In general you do hierarchical clustering and then evaluate the robustness of learning on different clusters.
The difficulty of perception is that you are misusing terminology. This is a fact.
If you had read my article, you would know about the terminology problem.... ignorance.
If you had read my article you would know about the terminology problem..... ignorance.
I tried, but it automatically closes itself at the introduction when the stated topic is denied at the very beginning of the article
I'm going to bed - wasting time discussing your desire to learn - well - I guess I don't have that kind of time.
Leaves are different :) Maybe it's hard to understand me, as I'm working on one task then another - and they are similar, but still different.
The point is that we build the tree not by the principle of greed - choosing from all split variants, but by evaluating historical data on the split for the stability of the final pattern. Thus, we narrow down the variants for selection. And from them we choose by some criterion - not necessarily by greed. We have a closing split - the range from and to the predictor. Everything that was selected at each iteration is saved and evaluated. We get statistics, which predictors and with which ranges participated more often in splitting - this is how the quantum table is selected (formed). Here with this table we train already on CatBoost. Alternative - binarisation of sample and training only on selected segments - there are difficulties in training by standard methods because of big sparsity of data. We can then get statistics, how each quantum segment from the selected ones will behave on new data - more of its class will be detected there or not relative to the average value in the sample (hence the probability is mentioned). Tests have shown that the less data is left for estimation, the less quantum segments with probability shift (with the same vector). The challenge is to keep the percentage of such quantum segments high in subsequent iterations, as the probability of choosing the correct split depends on it.
Experiments show that when building a tree, the order of predictors used in splitting is critical, which means that the method of the greedy principle will not often give the optimal solution.
Using a drowned term at will again?) Reducing the choices for split does not mean giving up greed (local optimum is always chosen). And using a different criterion, "taking into account sustainability", does not mean giving up greed.