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 Sources Selecting Data Exploring Data Transforming Data Unsupervised Modelling Building Classification 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 Glossary Bibliography Index

Priors

Sometimes the proportions of classes in a training set do not reflect their true proportions in the population. You can inform Rattle of the population proportions and the resulting model will reflect these.

The priors can be used to ``boost'' a particularly important class, by giving it a higher prior probability, although this might best be done through the Loss Matrix.

In Rattle the priors are expressed as a list of numbers that sum up to 1, and of the same length as the number of classes in the training dataset. An example for binary classification is 0.5,0.5.

The default priors are set to be the class proprtions as found in the training dataset.

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