Workflows

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

Discretization into Deviation Interval aro... (1)

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This process shows how numerical attributes can be discretized into intervals based on the standard deviation for each attribute around their mean values.

Created: 2010-06-22 | Last updated: 2010-06-22

Same Number of Examples per Class (Stratif... (1)

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This process can be used to sample examples from each class of the data set so that the number of examples per class is the same for all classes. The name of the label attribute is defined in the first "Set Macro" operator within the subprocess "Stratification". The result will be a stratified data set where each class is represented by the minimum number of examples for a single class minus 1 (due to calculation reasons in absolute sampling which is used here). The first two operators just...

Created: 2010-06-10

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

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

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

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

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

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

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