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

Impute

Imputation is the process of filling in the gaps (or missing values) in data. Often data will contain missing values, and this can cause a problem for some modelling algorithms. For example, the random forest option silently removes any entity with any missing value! For datasets with a very large number of variables, and a reasonable number of missing values, this may well result in a small, unrepresentative dataset, or even no dataset at all!

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When Rattle performs an imputation it will store the results in a variable of the dataset which has the same name as the variable that is imputed, but prefixed with IMP_. Such variables, whether they are imputed by Rattle or already existed in the dataset loaded into Rattle (e.g., a dataset from SAS), will be treated as input variables, and the original variable marked to be ignored.

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