Author: Michael Yee
Date: 12:37:30 03/09/04
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On March 09, 2004 at 11:50:39, Gian-Carlo Pascutto wrote: >On March 09, 2004 at 11:28:16, Michel Langeveld wrote: > >>Last week I worked hard on Nullmover and the ecm-gcp testsuite in particulair. >>I thought this testsuite is a fast way to the tune middlegame knowledge, kick >>things out and get things back in and do multiple testruns. >> >>I did the following: >>*1 kicked my kingsafety totally and use only pawnshelter >>*2 kicked my mobility totally out >>*3 added more information in the pawn struct > >Tuning your evaluation on a testsuite is fundamentally wrong. > >-- >GCP To borrow a phrase from Uri : "I disagree." :-) From a machine learning (or even plain regression) perspective, fitting a function to training data is fine as long as you make sure you're not overfitting (i.e., fitting noise). This is what training a model is all about. Additionally, I think there could be even less danger in the case of chess since the training data has no error (in theory). On the other hand, tuning the static eval for a very small training set could be dangerous if you're fitting more parameters than you have observations. But if you had a large enough training set, what would be the problem? You could always verify your new fitted eval on a validation test set. Michael P.S. Deep Thought's authors tuned its weights using GM games... http://www.tim-mann.org/deepthought.html
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