DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Neural Network

> library(nnet)
> ?nnet		# See example there

Consider a two-class problem. Build a neural network with

>

The average Matthew Correlation Coeffience can be used to gauge the performance of the neural network. The highr the value the better.

For an unbalanced class, for example where the ratio of class A to class B is about 3:100, we might decide to weight the under-represented class:

> sample.nn <- nnet(....... weights=ifelse(ds$class=="A", 100/3, 1)

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