This document discusses advanced processes and operators in RapidMiner including feature selection, splitting processes, OLAP operators, post processing operators, and preprocessing operators. Feature selection uses the backward elimination algorithm to test which attributes are relevant for building a better model. Processes can be split into learning and applying sections. OLAP operators support tasks like grouping, aggregation, and pivoting for multidimensional analysis. Post processing operators perform actions after modeling like cost-sensitive threshold selection. Preprocessing operators generate new features or clean data by imputing missing values.

1. RapidMiner5 2.4 Advanced processes and operators

2. Advanced Processes 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

3. Advanced Processes Here is how backward elimination works within RapidMiner: Enclose the cross-validation chain by a ‘FeatureSelection’ operator.

4. Advanced Processes Splitting a process: Learning Process Applying

5. Advanced Processes Splitting a process

6. Advanced Processes Splitting a process Learning Applying

7. OLAP operators 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.

8. OLAP operators OLAP operators supported by RapidMiner GroupBy Aggregation GroupedANOVA Atrribute2ExamplePivoting Example2AttributePivoting

9. Post Processing operators Postprocessing operators can usually be applied on models in order to perform some postprocessing steps like cost-sensitive threshold selection or scalingschemeslike Platt scaling.

10. Post Processing operators Post-Processing operators in RapidMiner WindowExamples- 2OriginalData FrequentItem- SetUnificator Uncertain- Predictions- Transformation PlattScaling ThresholdCreator ThresholdApplier ThresholdFinder

11. Preprocessing Operators 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.

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