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Handling data and time example
(1)
This process shows how date and time formats can be converted from arbitrary formats to other arbitrary formats with the operators "Nominal to Date" and "Date to Nominal".
Created: 2011-11-28
Plotting Training vs Testing Error (Loop +...
(1)
This process increases the parameter C of a support vector machine and hence also the risk for overfitting. It uses an outer loop operator for increasing the parameter value and an inner log operator for storing the current number of applications together with the current errors on the training and the testing data set. At the end of the process, the log data can be plotted (for example with the plotter "Scatter Multiple" with "Count" on the x-axis and both "Training Error" and "Testing Error...
Created: 2011-08-15
Iterate over Attribute Subsets and Store A...
(1)
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
Trim and Replace White Space
(1)
This process removed leading and trailing white spaces from an attribute and replaces inner white spaces of any type by a single space. Might be useful for text processing (if you are not using tokenization anyway...)
Created: 2011-07-07
Create Linear Combinations
(1)
This process creates new attributes based on all combinations of coefficients for two attributes, i.e. it produces all linear combinations c1 * att1 + c2 * att2 by looping through a set of possible values for c1 and c2 and all their different combinations.
Created: 2011-04-08
Rocchio
(1)
This process calculates the average values for all attributes grouped by the class (label). The resulting prototypes or centroids are then used for a nearest neighbor model which is applied again on the full data set for demonstration purposes.
Created: 2011-02-28
Count co-occurences in matrix with Aggrega...
(1)
The process counts and shows all combinations of values of two attributes and shows them in a count matrix. The process uses the operators Aggregate and Pivot for this purpose.
Created: 2011-01-15
Using R to add two columns
(1)
This process shows a simple R script which adds two columns of a data set with R. Of course this is much simpler by using the operator "Generate Attributes" which is done in parallel, but maybe some of you find this short process helpful in order to get started with R.
Created: 2010-12-10
| Last updated: 2010-12-10
Automatical Disabling / Enabling of Operat...
(1)
This meta process shows another possibility for automatically optimizing the process layout. The operator "OperatorEnabler" can be used to enable or disable one of its children. Together with one of the parameter optimization operators this can be used to check which operators should be used for optimal results. This is especially useful in order to determine which preprocessing operators should be used for a particular data set - learner combination or if you want to automatically test diffe...
Created: 2010-10-13
Extended Operations for Nominal Values
(1)
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
Pivoting with Consideration of Example Wei...
(1)
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)
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...
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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
Creation of New Attribute Depending on Val...
(1)
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)
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)
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)
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)
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
Weighted Score Tables
(1)
This process calculates a weighted score as known from SAP BW. The first operator generates data similar to that used in the links you provided. The next operator (Discretize) defines the different age groups. The next three operators map each age group to the value used for scoring. The last operator finally calculates the score and adds it as a new column to your data set.
Created: 2010-05-05
User: Ingo Mierswa
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