Neural Network Software Packages

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 0 votes I apologize in advance if this is a stupid question. it seems that a neural network would be ideally suited to this type of problem, though it might not provide the accuracy needed to win. i have read all the posts in the forum, many centered around sophisticated analytical tools like R ... but i've seen nothing on use of neural network packages. what am i missing? is anyone using NN software? why not? #1 | Posted 5 years ago Posts 2 Joined 5 Apr '11 | Email User
 0 votes You have to know what you are doing to use a NN.  They are prone to overfitting.  This isn't a problem if you know how to take care of it, but you might want to try something like randomForests - you actually have to try to mess them up :) You can use NN networks in R.  There are some windows based programs as well.  They are certainly fun to play around with, and I am sure some here will be using them. You might want to give R a shot. You can easily download and install it and in the other thread I have a script that will do everything for you (including loading in the data, making - you can relatively easily use a NN by just switching the model you use. http://www.heritagehealthprize.com/c/hhp/forums/t/607/r-questions/4122 Is the thread - you can even use it for a practice submission - you have 40 minutes before the counter resets for the day :) - and could do it by then.... http://cran.r-project.org/ There is no compiling,tarballing, pathsetting or any of that stuff - put the files in  MyDocuments(if you have windows) - and the code I posted will do everything including save the file you need to submit. I bet your score would even beat 1/3 of the leaderboard (I haven't tried it - but I bet it would) R in a Nutshell has a good couple chapters on Machine Learning as well. #2 | Posted 5 years ago Competition 20th Posts 194 | Votes 92 Joined 9 Jul '10 | Email User
 0 votes fitted RMSE was .405 It did not overfit - I bet you used: predict to get that .405 score - you need to get the OOB prediction (it doesn't matter/NA for the test data )- only the training data. use: rf.model$predicted for what you use to compute the training error. It won't usually overfit the data - but it isn't magic - if your variables aren't similar to what is in the test set - you can have issues (this would apply to any method). x <- predict(rf.model, your.data) # Use that for getting predictions for test data DO NOT use for training data y <- rf.model$predicted # will give you the OOB predictions for the training data (not possible to get for other data) Hope that makes sense As far as the trademark issue goes - I agree that a trademark has to deal with the name - not the software itself. So I don't mean I am using "Random Forest" - I am using an "ensemble of decision trees" - that happen to be generated - you know - randomly - like in a forest. I am not say no one can complain for other reasons - but the name is the only thing that would be an issue for a trademark. #7 | Posted 5 years ago Competition 20th Posts 194 | Votes 92 Joined 9 Jul '10 | Email User