Machine learning in trading: theory, models, practice and algo-trading - page 3397
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I also plan to experiment not with positional matchmaking, as in the article, but with Propensity score. This will allow to calibrate probabilities at the same time.
Then I'm going to use that google liba to see what I can get out of it.
Maybe later I'll roll out the results.
I also plan to experiment not with positional matchmaking, as in the article, but with Propensity score. This will allow to calibrate probabilities at the same time.
In theory, you can search for and match samples through it
For example, randomly mark one piece of the sample as 0 and another as 1. Teach the NS to separate to classify which sample belongs to which sample. This is also called Adversarial validation.
Ideally, the NS should not identify the sample, the error should be around 0.5. This means that the original sample is well randomised.
Anything in the neighbourhood of 0.5 is good, this data can be used for training. The extreme values are outliers.
Then for each "probability" you can calculate the percentage of guessed cases.
So far, it's a bit mind-blowing to take this approach.
An interesting ongoing contest - for those who want to compare their success in predicting quotes with other participants.
Interesting ongoing competition - for those who want to compare their success in predicting quotes with other participants.
This link has already been here many times
I didn't remember it - I guess it wasn't clear then what to do, but now I read the help and it became clearer. Anyway, it's a fact that this idea has been working for a long time. As I understand it, they pay there with some kind of crypto for good forecasts.
The disadvantage, of course, is that the code is open and must be transferred for participation.
The future is here. I'm running Google's LLM locally. Now I don't need a wife and friends.
The future is here. I'm running Google's LLM locally. Now I don't need a wife and friends.
https://blog.google/technology/developers/gemma-open-models/
A good summarisation of the whole thread
A good summarisation of the whole thread