brnn package (Bayesian regularization for feed-forward neural networks)
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Last seen 3.1 years ago
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I'm using the "brnn" package (Bayesian Regularized Neural Networks), in particular I run the train() function from caret package. My data are stored in a data.frame object and I run the caret function using the "formula" way y ~. (in the "point" there are seven variables that we can call here x1,x2,x3....x7) I would like to understand how to write the formula of brnn method using the values obtained from R. I obtained the following results.

  • MY_results$Model$finalModel$theta
  • MY_results$Model$finalModel$alpha
  • MY_results$Model$finalModel$beta
  • MY_results$Model$finalModel$gamma
  • MY_results$Model$finalModel$Ed
  • MY_results$Model$finalModel$Ew
  • MY_results$Model$finalModel$F_history
  • MY_results$Model$finalModel$reason
  • MY_results$Model$finalModel$epoch
  • MY_results$Model$finalModel$neurons
  • MY_results$Model$finalModel$p
  • MY_results$Model$finalModel$n
  • MY_results$Model$finalModel$npar
  • MY_results$Model$finalModel$x_normalized
  • MY_results$Model$finalModel$x_base
  • MY_results$Model$finalModel$x_spread
  • MY_results$Model$finalModel$y_base
  • MY_results$Model$finalModel$y_spread
  • MY_results$Model$finalModel$y
  • MY_results$Model$finalModel$normalize
  • MY_results$Model$finalModel$call
  • MY_results$Model$finalModel$xNames
  • MY_results$Model$finalModel$problemType
  • MY_results$Model$finalModel$tuneValue
  • MY_results$Model$finalModel$obsLevels
  • MY_results$Model$finalModel$param

I tried to reproduce the brnn formula described inĀ, but I'm not sure how to consider variables x1,x2....x7.

Thank you in advance for any kind of suggestions.


R machine learning brnn caret • 606 views

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