DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining 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 Glossary Bibliography Index

Troubleshooting

Rattle is open source, freely available, software, and benefits from user feedback and user contributions. It is also under constant development and improvement. Whilst every effort is made to ensure Rattle is error and bug free, there is always the possibility that we will find a new bug. This is then the opportunity for us to review the code and to improve the code. At the very least though, if you find a problem with Rattle, contact the author to report the problem. At best, feel free to hunt down the source of the problem in the R code and provide the solution to the author, for all to benefit.

In this chapter we record some known issues that you may come across in using Rattle. These aren't bugs as such but rather known issues that a data miner is likely to come across in building models.

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