RapidMiner5<br />2.4 Advanced processes and operators<br />
Advanced Processes<br />Feature Selection: Let us assume that we have a dataset with numerous attributes. We would like to...
Advanced Processes<br />Here is how backward elimination works within RapidMiner:<br />Enclose the cross-validation chain ...
Advanced Processes<br />Splitting a process:<br />Learning<br />Process<br />Applying<br />
Advanced Processes<br />Splitting a process<br />
Advanced Processes<br />Splitting a process<br />Learning<br />Applying<br />
OLAP operators <br />OLAP (Online Analytical Processing) is an approach to quickly providing answers to analytical queries...
OLAP operators <br />OLAP operators supported by RapidMiner<br />GroupBy<br />Aggregation<br />GroupedANOVA<br />Atrribute...
Post Processing operators<br />Postprocessing operators can usually be applied on models in order to perform some postproc...
Post Processing operators<br />Post-Processing operators in RapidMiner<br />WindowExamples-<br />2OriginalData<br />Freque...
Preprocessing Operators<br />Preprocessing operators can be used to generate new features by applying functions on the exi...
More Questions?<br />Reach us at support@dataminingtools.net<br />Visit: www.dataminingtools.net<br />
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RapidMiner: Advanced Processes And Operators

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RapidMiner: Advanced Processes And Operators

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RapidMiner: Advanced Processes And Operators

  1. 1. RapidMiner5<br />2.4 Advanced processes and operators<br />
  2. 2. Advanced Processes<br />Feature Selection: Let us assume that we have a dataset with numerous attributes. We would like to test, whether all of these attributes are really relevant, or whether we can get a better model by omitting some of the original attributes. This task is called feature selection and the backward elimination algorithm is an approach that can solve it for you<br />
  3. 3. Advanced Processes<br />Here is how backward elimination works within RapidMiner:<br />Enclose the cross-validation chain by a ‘FeatureSelection’ operator.<br />
  4. 4. Advanced Processes<br />Splitting a process:<br />Learning<br />Process<br />Applying<br />
  5. 5. Advanced Processes<br />Splitting a process<br />
  6. 6. Advanced Processes<br />Splitting a process<br />Learning<br />Applying<br />
  7. 7. OLAP operators <br />OLAP (Online Analytical Processing) is an approach to quickly providing answers to analytical queries that are multidimensional in nature. Usually, the basics of OLAP is a set of SQL queries which will typically result in a matrix (or pivot) format.<br />
  8. 8. OLAP operators <br />OLAP operators supported by RapidMiner<br />GroupBy<br />Aggregation<br />GroupedANOVA<br />Atrribute2ExamplePivoting<br />Example2AttributePivoting<br />
  9. 9. Post Processing operators<br />Postprocessing operators can usually be applied on models in order to perform some postprocessing steps like cost-sensitive threshold selection or scalingschemeslike Platt scaling.<br />
  10. 10. Post Processing operators<br />Post-Processing operators in RapidMiner<br />WindowExamples-<br />2OriginalData<br />FrequentItem-<br />SetUnificator<br />Uncertain-<br />Predictions-<br />Transformation<br />PlattScaling<br />ThresholdCreator<br />ThresholdApplier<br />ThresholdFinder<br />
  11. 11. Preprocessing Operators<br />Preprocessing operators can be used to generate new features by applying functions on the existing features or by automatically cleaning up the data replacing missing values by, for instance, average values of this attribute.<br />
  12. 12. More Questions?<br />Reach us at support@dataminingtools.net<br />Visit: www.dataminingtools.net<br />

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