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

Score Option

[width=]rattle-evaluate-savescore

Often you will want to apply a model to a dataset to generate the scores for each entity in the dataset. Such scores may be useful for further exploration in other tools, of for actual deployment.

The Score radio button allows you to score (i.e., to generate probabilities for each entry in) a dataset. The specific dataset which is scored is that which is identified with the Data option. In the above example, the model will be used to score the Testing dataset. You can score the actual Training dataset, a dataset loaded from a CSV data file, or from a dataset already loaded into R.

Rattle will generate a CSV file containing the ``scores'' for the 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 itself. This score will be a number between 0 and 1.

Note the status bar in the sample screenshot has identified that the score file has been saved to the suitably named file. The file name is derived from name of the dataset (perhaps a source data csv filename or 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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