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 Select Data Explore Transform Classify Unsupervised Text Mining 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 Cluster Analysis 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 Glossary Bibliography Index

Tuning Options

There are quite a few options exposed by Rattle for boosting our decision tree model. We will explore how these can help us to build the best model.

It is not always clear exactly how many trees we should build for our ensemble. The default for ada is to build 50 trees. But is that enough? The Boost functionality in Rattle allows the ensemble of trees to be added to, so that we can easily explore whether more trees will improve the performance of the model. To do so, simply increase the value specified in the Number of Trees text box and click the Continue button. This will pick up the model building from where it was left off and build as many more trees as is needed to get up to the specified number of trees.

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