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 Post subject: How can machine learning algorithms learn folding probabilites?
PostPosted: Fri Mar 01, 2013 7:33 pm 
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Hello everybody,

I read a couple of posts about opponent modelling on this forum, but I am probably missing something crucial.

I am currently gathering data from observed hands and if the hands come to showdown, I add the handstrength (integer 0 to 5) and
number of outs (improve to pair, to top_pairish hands, to nuts) the players had on the given street to the database.

So my database has columns like:
betround, boardwetness, pot, effectivestacks, xbet_before, betsize_pre, betsize_post, handstrength, outs

My idea is to train the learning algorithm offline and online I want to retrieve the probability for actions (fold; checkorcall; betorraise).
situation_vector -> type of action (e.g 1; 0; 0)

But how do I train a learning algorithm like NN,SVM,RVM,etc so it will learn for example when someone folds when I only have handstrength data
for showdown hands.

For example, you have an opponent that always folds air to a bet. How is that directly learnable without showdown information?
I read somewhere you just leave handstrength as NaNs, but that didnĀ“t make much sense to me.


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 Post subject: Re: How can machine learning algorithms learn folding probabilites?
PostPosted: Fri Mar 01, 2013 11:41 pm 
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http://pokerai.org/pf3/viewtopic.php?f= ... ilit=kleij


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 Post subject: Re: How can machine learning algorithms learn folding probabilites?
PostPosted: Sat Mar 02, 2013 2:23 am 
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Thanks! I foolishly skipped the chapters after the MCTS algorithm the first time I read it. :xx08


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