pharmine

1. Add ArffExampleSource operator (IO->Examples) to Root.
• Configure it to load a dataset from a file.
• Set the value of the label_attribute to the class column.
2. Add SimpleValidation operator (Validation) to Root and configure it by setting the split_ratio value to 0.8.
• Add OperatorChain operator to SimpleValidation operator.
• Add ArffExampleSetWriter operator (IO->Examples) to the current OperatorChain operator and configure it to save the training set to a file.
• Add NearestNeighors operator (Learner->Supervised->Lazy) to the current OperatorChain operator.
• Add another OperatorChain operator to SimpleValidation operator.
• Add ArffExampleSetWriter operator (IO->Examples) to the current OperatorChain operator and configure it to save the testing set to a file.
• Add ModelApplier operator to the current OperatorChain operator.
• Add Performance operator (Validation) to the current OperatorChain operator.
3. Run.

In RapidMiner, partitioning of dataset must be accompanied by learning a model from the newly created training set and evaluation of the model on the newly created testing set. There are no operators which can just partition the dataset into a training set and testing set without model building and evaluation. Although RapidMiner has a lot of operators, its selection of operators for partitioning datasets seem to be rather limited. For example, it does not contain other algorithms, like the Kennard and Stone algorithm, for partitioning datasets.

This entry was posted by Yap Chun Wei on Sunday, March 30th, 2008 at 9:52 pm and is filed under Data mining, Review. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.