User: Sebastian land

RBioconductor Tutorial Process (1)

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This Process illustrates how to create complex Data Analysis processes using R Scripts. In this case the Bioconductor Basic Tutorial is splitted into reusable components.

Created: 2011-01-03

Constructing user defined linear regressio... (1)

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This process shows how one can construct a user defined Linear Regression model using the execute script operator

Created: 2010-09-02 | Last updated: 2010-09-02

Finding all Examples that have duplicate v... (1)

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This process will retrieve all examples, who have identical values in a specific attribute. For testing, the following data can be writen into the file, that will be read by the Read CSV operator: CID,Value 3596,X 4054,X 4054,X 3000,S 3000,T 3000,U 32135,S The target of this process is to return the two examples having the same value in the CID column. To achieve this, first a real id is generated by the generate id. After this, we have to find all duplicates: For this we first remove dupl...

Created: 2010-06-18

Preprocessing nominal data for frequent it... (1)

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This process will first create artificial data that can be compared to usual data loaded for frequent item set mining: Nominal Data with a true and false value, but differently mapped to internal indices. For ItemSet Mining these must be preprocessed to avoid problems: First they have to be transformed to Binominal Attributes, then it has to be defined, which is the positive and the negative value.

Created: 2010-06-16 | Last updated: 2010-06-16

Prepending common prefix to attributes (1)

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This process shows how one can add a common prefix to a subset of attributes. First a subset might be defined by the attribute set selection parameters of the rename by replacing operator. Then one can make use of the capturing group functionality of regular expressions to insert the original name into the replacement.

Created: 2010-06-04

Combining nominal attributes with missing (1)

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This process will show how one can combine nominal values of attributes that contain missing values. A generate attribute operator is used and hence forbidden characters must first be replaced. After this, a condition in the generation ensures that no question mark (standing for missing value) will be shown in the result, if any of the two combined attributes is known.

Created: 2010-06-04

Plot round results of a backward elimination (1)

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This process will perform a backward elimination and logs all performance and deviation results of each round. This way, you can use the visualizations of rapidminer to asses the performance gain.

Created: 2010-05-26

Setting an attribute value in a specific E... (1)

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This process will first generate an artificial dataset and tag it with ids. Then a Filter Examples Operator is used to get a dataset with exactly one example identified by it's id. Then a value is set in this example. Since the change in the data will be reflected in all views of the example set, a simply copy is passed by to the process' output. If you take a look at the attributes of the example with id 5, you will find the 12 there.

Created: 2010-05-14

Correct Attribute Type to Binominal (1)

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This process will first generate some artificial data to show a commonly known problem: Some attributes have only two values, but are not correctly stored as Binominal, instead RapidMiner recognizes them as nominal. In order to use them for some special operators, we have to change this to binominal. We can achieve this by using the Nominal to Binominal operator with the the parameter transform_binominals switched off. Please take a look at the data before and after the Nominal to Binominal o...

Created: 2010-05-14

Defining positive class with Remap Binominal (1)

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This process shows how one can use the Remap Binominal operator to define which label value is treated as the positive class.

Created: 2010-05-11

Generating Example Weights depending on label (1)

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This process uses a Generate Attribute operator for assigning new weights to examples. It uses the if condition of this operator to distinguish between labels. This can be especially useful if you have a strong bias in your class frequency, which can harm learning. Please note that you must use a learning algorithm that supports weighted examples.

Created: 2010-05-03

Looping over Examples for doing de-aggrega... (1)

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This process is based on (artificially generated) data that looks like it has been aggregated before. The integer attribute Qty specifies the quantity of the given item that is represented by the rest of the example. The process now loops over every example and performs on each example another loop, that will append the current example to a new example set. This example set has been created as empty copy of the original example set, so that the attributes are equally. To get access to and rem...

Created: 2010-04-29