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2009 May 12
0
neural network not using all observations
I am exploring neural networks (adding non-linearities) to see if I can
get more predictive power than a linear regression model I built. I am
using the function nnet and following the example of Venables and
Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have
standardized variables (z-scores) such as assets, age and tenure. I have
other variables that are binary (0 or 1). In
2009 May 12
0
FW: neural network not using all observations
As a follow-up to my email below:
The input data frame to nnet() has dimensions:
> dim(coreaff.trn.nn)
[1] 5088 8
And the predictions from the neural network (35 records are dropped -
see email below for more details) has dimensions:
> pred <- predict(coreaff.nn1)
> dim(pred)
[1] 5053 1
So, the following line of R code does not work as the dimensions are
2009 May 12
0
How do I extract the scoring equations for neural networks and support vector machines?
Sorry for these multiple postings.
I solved the problem using na.omit() to drop records with missing values
for the time being. I will worry about imputation, etc. later.
I calculated the sum of squared errors for 3 models, linear regression,
neural networks, and support vector machines. This is the first run.
Without doing any parameter tuning on the SVM or playing around with the
number of