Workflows

Iterate over Attribute Subsets and Store A... (1)

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This process iterates over all possible feature subsets and stores a) the names of all attribute subsets, b) the number of used features, and c) the achieved performance in a log table which can then be further analyzed.

Created: 2011-07-07

Extended Operations for Nominal Values (1)

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This process shows examples for the extended operations for nominal values coming with one of the next RapidMiner updates (5.0.011 or 5.1.000). The operations are performed with the operator "Generate Attributes" and can be used directly within the expressions for the new attributes. The supported functions include Number to String [str(x)], String to Number [parse(text)], Substring [cut(text, start, length)], Concatenation [concat(text1, text2, text3...)], Replace [replace(text, what, by)],...

Created: 2010-10-05

Evaluating Multiple Models with Looped X-V... (1)

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This process shows how multiple different models can be evaluated with cross validation runs. This allows for the comparison of, for example, three prediction model types (e.g., ANN, SVM and DT) all at once under a x-fold cross validation. Having said that, the process actually performs the same cross validation several times using each time a different modeling scheme. It makes use of loops, collections, subprocess selection and macros and is therefore also an interesting showcase for more ...

Created: 2010-05-15

Pivoting with Consideration of Example Wei... (1)

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This process shows how the Pivoting operator can also consider example weights encoded by a special attribute with the role 'weight'. The first operators only create a demo data set for this purpose, the actual pivoting is performed by the last operator only. I recommend to place a breakpoint before the Pivoting and check the data (structure) in order to see how the Pivoting handles the weights.

Created: 2010-07-07

Pivoting (1)

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This process shows the basics of Pivoting. A data set with three columns is loaded and partially generated. Afterwards, the data is rotated and missings are replaced by zero.

Created: 2010-05-15

Convert Nominal to Binominal to Numerical (1)

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This is a standard preprocessing subprocess taking nominal (categorical) attributes and introduces binominal dummy attributes before those are transformed to numerical which can be then used by learning schemes like SVM or Logistic Regression.

Created: 2010-05-15

Discard Attribute with More than x% Missin... (1)

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This process loops over all attributes and calculates the fraction of missings for each attribute. If this fration is larger than the fraction defined in the first "Set Macro" operator (macro: max_unknown), the attribute will be removed from the example set.

Created: 2010-05-15

Creation of New Attribute Depending on Val... (1)

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The process shows the usage of the operator "Generate Attributes" in combination with an "if - then - else" condition and nominal values. The values "value0" and "value1" are mapped to "T1", other values are mapped to "T2" for the new attribute.

Created: 2010-06-01

Transform Attribute Names to Upper Case (S... (1)

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This process uses a Script operator which transforms the attribute names of the input example set into UPPER case.

Created: 2010-06-06

Transform Attribute Names to lower Case (S... (1)

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This process uses a Script operator which transforms the attribute names of the input example set into lower case.

Created: 2010-06-06