- #1
tomeram
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Hey
I have a neural network model that produces as an output a vector of 169 variables which represents the probabilty of having a certain hand in poker (2 random cards dealt from a regular deck - 169 possibilities if considering only if the cards are from same suit or not).
The model predict for each spesific situation and action made by a player in the game the distribution of having all possible hands.
I kept a random sample from the data for testing the model, and now I want to test it. Each row in the testing set contains the data of the situation, the action the player made and the hand he had, however the model produce a vector of 169 values (which represent the probabilty of having each of the possible hand).
I am looking for a statistical method to estimate the accuracy of the model - some kind of method that can say what is probabilty that the observation came from the distribution produced by the model.
Thanks
Tomer
I have a neural network model that produces as an output a vector of 169 variables which represents the probabilty of having a certain hand in poker (2 random cards dealt from a regular deck - 169 possibilities if considering only if the cards are from same suit or not).
The model predict for each spesific situation and action made by a player in the game the distribution of having all possible hands.
I kept a random sample from the data for testing the model, and now I want to test it. Each row in the testing set contains the data of the situation, the action the player made and the hand he had, however the model produce a vector of 169 values (which represent the probabilty of having each of the possible hand).
I am looking for a statistical method to estimate the accuracy of the model - some kind of method that can say what is probabilty that the observation came from the distribution produced by the model.
Thanks
Tomer