DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process Rattle R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Scoring Data

Often you will want to apply a model to a dataaset to generate scores for use in other tools. The Score radio button allows you to score (i.e., to generate probabilities for each entry in) a dataset. Rattle will generate a CSV file containing these ``scores,'' and we refer to this process as scoring a dataset. Each line of the CSV file will consist of a comma separated list of all of the variables that have been identified as Idents in the Variables tab, followed by the score.

Note the status bar in the sample screenshot has identified that the score file has been saved to the file. The file name is derived from name of the dataset (perhaps a source data csv filename of the name of an R data frame), whether it is a test or training dataset, the type of model and the type of score.

The output looks like:

  ID,predict
  98953270,0.104
  12161980,NA
  96316627,0.014
  54464140,0.346
  57742269,0.648
  19307037,0.07
  61179245,0.004
  36044473,0.338
  19156946,0.33

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