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In machine learning, supervised learning is a technique for deducing a function from [https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets training data]. The training data consist of pairs of input objects and desired outputs . After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set <ref>[https://en.wikipedia.org/wiki/Supervised_learning Supervised learning from Wikipedia]</ref>. In computer games and chess, supervised learning techniques were used in [[Automated Tuning|automated tuning]] or to train [[Neural Networks|neural network]] game and chess programs. Input objects are [[Chess Position|chess positions]]. The desired output is either the supervisor's move choice in that position ([[Automated Tuning#MoveAdaption|move adaption]]), or a [[Score|score]] provided by an [[Oracle|oracle]] ([[Automated Tuning#ValueAdaption|value adaption]]).