Simon Fischer's Workflows
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Transaction Analysis Demo from RM 5 Intro Day (1)
This is the demo process presented at the RapidMiner 5 Intro Day. It combines customer segmentation with direct mailing.
It loads some transaction data, aggregates and pivotes the data so it can be used by a clustering to perform a customer segmentation. Then, additional data is joined with the clustered data. First, response/no-response data is joined, and them some additional information about the users is added. Finally, customers are classified into response/no-response classes.
The dat...
Created: 2010-04-30 | Last updated: 2010-05-05
Stacking (1)
RapidMiner supports Meta Learning by embedding one or several basic learners as children into a parent meta learning operator. Here, we use a three base learners inside the stacking operator: decision tree induction, linear regression, and a nearest neighbours classifier. Finally, a Naive Bayes learner is used as a stacking learner which uses the predictions of the preceeding three learners to make a combined prediction.
Created: 2010-04-29
Performs a crossvalidation on a given data set with nominal label, using a Support Vector Machine as a learning algorithm. Inside the cross validation, the first subprocess generates an SVM model, and the second subprocess evaluates it. applying it on a so-far unused subset of the data and counting the misclassifications.
Created: 2010-04-29