Machine learning in trading: theory, models, practice and algo-trading - page 394
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If you start something for a month - use an uninterrupted power supply for your computer, I once in 2 weeks of calculations cut off the light)))
And GPU version don't expect much, to rewrite the code seems to me longer and if the author has not done, it is unlikely that someone else will finish this task to the end.
Well, the author paralleled everything, now you just need to run it. I started it for 3 days and got a model with 9 inputs, which is a record to be honest. I don't really want to optimize it for so long. But as they say. The market demands it. Therefore, looking for power, if anyone is able to optimize dataset on the optimizer, and even on the cores as 20-30, I would be very grateful.
Mihail Marchukajtes:
Learning days, weeks
Apparently your algorithm is not optimal, on such small datasets, you can safely use bruteforcing algorithms such as Knn, which are quasi-optimal, if the algorithm runs slower than Knn it is probably a bad ML algorithm or poorly configured. On such a dataset, the whole training cycle and run of the whole set shouldn't take more than a second.
I explained above. 100 splits each split is trained 1000 epochs, etc. You're just fixated on a single training of a neuron, while the essence of optimizer is to calculate dataset so that there would be no questions about its suitability. I.e. it twists this file up and down figuratively, and you keep comparing it to a single training of a single neuron. IMHO. It is essentially a system of AI, in which in addition to training a neuron, there are all sorts of optimization and preprocessing, and the training itself runs hundreds of times. If anything....
I explained above. 100 splits, each split is trained in 1000 epochs, etc. You're just fixated on a single neuron training, while the essence of optimizer is to calculate dataset so that there is no question about its suitability. I.e. it twists this file up and down figuratively, and you keep comparing it to a single training of a single neuron. IMHO. It is essentially a system of AI, in which in addition to training a neuron, there are all sorts of optimization and preprocessing, and the training itself runs hundreds of times. If anything....
MLP is guessing 95% of the time... I don't think you're doing the right bike) No offense.
You have a mistake.
The very first column in the table is the row number, and you can't use that column in prediction, but it's only required for jPrediction for some reason.
The target is distributed so that the first half of the lines is class 0, and the second half of the lines is class 1. So the neuronka just remembers that if the line number is less than 228 it's class 0, otherwise it's class 1.
You have a mistake.
The very first column in the table is the row number, and you can't use this column in prediction, but it's required only for jPrediction for some reason.
The target is distributed so that the first half of the lines is class 0, and the second half of the lines is class 1. So the neuronics just remembers that if the line number is less than 228 then it's class 0, otherwise it's class 1.
And by the way, yes. Didn't notice that it's just a number.
Without it Inputs to keep: 4,50,53,59,61,64,92,98,101,104,
Average error in the training (60.0%) plot =0.269 (26.9%) nLearns=2 NGrad=7376 NHess=0 NCholesky=0 codResp=2
Mean error on validation (20.0%) plot =0.864 (86.4%) nLearns=2 NGrad=7376 NHess=0 NCholesky=0 codResp=2
Average error on test (20.0%) plot =0.885 (88.5%) nLearns=2 NGrad=7376 NHess=0 NCholesky=0 codResp=2
Clearly overtraining. So, we have to make some other sifting of inputs.
Maybe sift by the weight of the inputs? Like you did for the problem in the first post of the thread...
I'm trying to rewrite R script, which you've attached, so that it could determine names and number of columns... but I don't have enough knowledge of R.
I'm trying to rewrite the R script that you attached, so that it determines the names and number of columns... but I don't know enough R.
I was still starting to learn R back then, the script is almost entirely generated in rattle (visual environment for datamining in R), that's why it's so complex and customized for all occasions.
This is the one
should be changed to...
And it should be ok.
In general, it's a bad approach, you shouldn't define importance of inputs that way. For some reason it worked that time, but it never helped me again.
It is better to define the importance of predictors as follows
The results of the importance assessment are as follows. The higher the predictor in the table, the better. OnlyVVolum6, VDel1, VVolum9, VQST10 passed the test.
In rattle we can build 6 models at once on these 4 predictors, and SVM shows accuracy of about 55% on validation and test data. Not bad.
MLP gets it right 95% of the time... I don't think you're making the right bike) No offense.
I make my own bike too, but based on decades of proven MLP (which they say is obsolete and needs something cooler to work on).
And try alglib decision trees too, they count faster and have better counts than mlp. Diplerning is also faster, but not in alglib.
The main thing is the speed/quality ratio, what's the point of waiting for a week or even a day or even an hour... you'll never find the optimal combination that way.) Model should take a few seconds to learn, then you can use genetics for autosimulation of parameters or predictors, then it's the true AI, otherwise it's rubbish)