Author Topic: Parameter Selection and Cross-Validation  (Read 1927 times)

Offline Holly

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Parameter Selection and Cross-Validation
« on: August 01, 2023, 22:31:51 »

Are there any tutorials or examples that outline a process for performing parameter selection for a Bayesian Network? (i.e. test different subsets of potential input variables and find the subset that gives the best model performance evaluated using something like k-fold cross validation, for example).

Offline Anders L Madsen

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Re: Parameter Selection and Cross-Validation
« Reply #1 on: August 02, 2023, 16:49:55 »
Hello Holly,

We do not have a tutorial showing the process for performing feature selection for a Bayesian network.

If we assume that you are considering a classification problem, then one approach is a wrapper search. Start with a model containing only the target variable and iteratively adding the feature which improves the score the most. Stop when there is no further improvement.

You can split the data into train, validate and test sets to measure the performance.

Best regards