NeuralTools and Neural Networks
When using NeuralTools, neural networks are developed and used in
four steps:
• Data Preparation - The data you use in NeuralTools is
defined in data sets. A Data Set Manager is used to set up
data sets so they can be used over and over again with your
neural networks.
• Training – With training, a neural network is generated from
a data set comprised of cases with known output values. This
data often consists of historical cases for which you know the
values of output/dependent variable.
• Testing – With testing, a trained neural network is tested to
see how well it does at predicting known output values. The
data used for testing is usually a subset of your historical
data. This subset was not used in training the network. After
testing, the performance of the network is measured by
statistics such as the % of the known answers it correctly
predicted.
• Prediction - A trained neural network is used to predict
unknown output values. Once trained and tested, the
network can be used as needed to predict outputs for new
case data.
Training and testing are an iterative, sometimes time-intensive
process. Typically, you may train several different times with
different settings in order to generate a neural network that tests best.
Once you have your "best net" you can quickly use it for predicting.
Now, let's look at how NeuralTools works in Excel and how you
define data sets and train and test neural networks using those data
sets. Then we will predict unknown output values using trained
networks.