Arppe [EMAIL PROTECTED]
Sent: Tuesday, 8 May, 2007 12:36:20 PM
Subject: Re: [R] Neural Nets (nnet) - evaluating success rate of predictions
On Mon, 7 May 2007, [EMAIL PROTECTED] wrote:
Date: Sun, 6 May 2007 12:02:31 + (GMT)
From: nathaniel Grey [EMAIL PROTECTED]
However what I really want
On 5/7/07, Bert Gunter [EMAIL PROTECTED] wrote:
Folks:
If I understand correctly, the following may be pertinent.
Note that the procedure:
min.nnet = nnet[k] such that error rate of nnet[k] = min[i] {error
rate(nnet(training data) from ith random start) }
does not guarantee a classifier
Nathaniel,
On Mon, 7 May 2007, [EMAIL PROTECTED] wrote:
Date: Sun, 6 May 2007 12:02:31 + (GMT)
From: nathaniel Grey [EMAIL PROTECTED]
However what I really want to know is how well my nueral net is
doing at classifying my binary output variable. I am new to R and I
can't figure out how
On 5/6/07, nathaniel Grey [EMAIL PROTECTED] wrote:
Hello R-Users,
I have been using (nnet) by Ripley to train a neural net on a test dataset,
I have obtained predictions for a validtion dataset using:
PP-predict(nnetobject,validationdata)
Using PP I can find the -2 log likelihood for the
well, how to do you know which ones are the best out of several hundreds?
I will average all results out of several hundreds.
On 5/7/07, hadley wickham [EMAIL PROTECTED] wrote:
On 5/6/07, nathaniel Grey [EMAIL PROTECTED] wrote:
Hello R-Users,
I have been using (nnet) by Ripley to train a
Pick the one with the lowest error rate on your training data?
Hadley
On 5/7/07, Wensui Liu [EMAIL PROTECTED] wrote:
well, how to do you know which ones are the best out of several hundreds?
I will average all results out of several hundreds.
On 5/7/07, hadley wickham [EMAIL PROTECTED] wrote:
Nonclinical Statistics
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of hadley wickham
Sent: Monday, May 07, 2007 5:26 AM
To: Wensui Liu
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Neural Nets (nnet) - evaluating success rate of predictions
Pick the one
Hello R-Users,
I have been using (nnet) by Ripley to train a neural net on a test dataset, I
have obtained predictions for a validtion dataset using:
PP-predict(nnetobject,validationdata)
Using PP I can find the -2 log likelihood for the validation datset.
However what I really want to know
