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Jon Dart

Joined: 10 Mar 2006
Posts: 3401
Location: http://www.arasanchess.org

 Post subject: Re: tuning for the uninformed    Posted: Sat Dec 02, 2017 5:56 pm The theory of logistic regression with discrete outcomes (dependent variables) deals mainly with three cases: 1. binary (0 or 1) outcomes 2. ordinal outcomes (such as poor, average, good). 3. discrete but unordered outcomes. None of these commonly use a squared error distance to measure goodness of fit, as the Texel method does. However, our problem is basically 2, but with a twist: there are three possible values for a game (0, 0.5 or 1), but the values are meaningful in the sense that 0.5 is really equidistant between 0 and 1. So in that case the Texel method may be a reasonable approach, it is just not as theoretically grounded. As for using the eval as an oracle: yes, you can do this. Your label is then the oracle's value and you regress to find the best match between predicted and the oracle value. All this changes in the whole procedure is the "loss function" that measures goodness of fit. You could use mean absolute or squared difference between predicted and oracle eval, for example. Yet another approach is to make predicted moves based on your eval match actually played moves by an "oracle" such as a strong program or a strong human player. This approach has been used in Shogi - there is a paper by Hoki and Kaneko. But realize that then you are really solving a different problem. The regression will make your eval conform to the oracle's eval. In effect, this is a fancy way of reverse engineering the oracle's eval. It may or may not make your eval predict game results better, or actually play better, but if the oracle is much stronger then it likely will cause improvement. --Jon
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Folkert van Heusden Thu Nov 23, 2017 10:37 am
Henk van den Belt Thu Nov 23, 2017 11:49 am
Mehdi Amini Thu Nov 23, 2017 11:49 am
Henk van den Belt Thu Nov 23, 2017 11:53 am
Folkert van Heusden Thu Nov 23, 2017 12:26 pm
Henk van den Belt Thu Nov 23, 2017 12:44 pm
Alexandru Mosoi Thu Nov 23, 2017 2:07 pm
Folkert van Heusden Thu Nov 23, 2017 2:16 pm
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Sander Maassen vd Brink Thu Nov 23, 2017 6:47 pm
Henk van den Belt Thu Nov 23, 2017 7:21 pm
Sander Maassen vd Brink Thu Nov 23, 2017 7:26 pm
Henk van den Belt Thu Nov 23, 2017 7:36 pm
Sander Maassen vd Brink Thu Nov 23, 2017 7:43 pm
Martin Fierz Tue Dec 12, 2017 9:17 pm
Álvaro Begué Tue Dec 12, 2017 10:47 pm
Folkert van Heusden Fri Nov 24, 2017 9:15 am
Sander Maassen vd Brink Fri Nov 24, 2017 9:30 am
Robin Messemer Fri Nov 24, 2017 11:04 pm
Henk van den Belt Sat Nov 25, 2017 11:30 am
Robin Messemer Sat Nov 25, 2017 1:58 pm
Henk van den Belt Sat Nov 25, 2017 2:48 pm
Robert Pope Mon Nov 27, 2017 5:38 pm
Álvaro Begué Mon Nov 27, 2017 5:52 pm
Alexandru Mosoi Mon Nov 27, 2017 9:56 pm
Robert Pope Tue Nov 28, 2017 4:41 pm
Robin Messemer Tue Nov 28, 2017 7:23 pm
Álvaro Begué Tue Nov 28, 2017 7:37 pm
Jon Dart Thu Dec 14, 2017 3:09 pm
Álvaro Begué Thu Dec 14, 2017 3:15 pm
Jon Dart Sat Dec 02, 2017 3:02 pm
Jon Dart Sat Dec 02, 2017 2:58 pm
Erin Dame Sat Dec 02, 2017 10:50 am
Jon Dart Sat Dec 02, 2017 2:54 pm
Erin Dame Sat Dec 02, 2017 5:14 pm
Re: tuning for the uninformed Jon Dart Sat Dec 02, 2017 5:56 pm
Jon Dart Sat Dec 02, 2017 8:09 pm
Matthew R. Brades Sun Dec 03, 2017 10:09 am
Álvaro Begué Sun Dec 03, 2017 1:03 pm
Jon Dart Sun Dec 03, 2017 3:47 pm

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