DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Installing GTK, R, and Rattle 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)

Note that we must do any scaling of continuous before using nnet since it is not done inside nnet (unlike ksvm).

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