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LDA (Latent Dirichlet Allocation) Operator & Gradient Boost

CraigBostonUSACraigBostonUSA Administrator, Employee, Member Posts: 34   RM Team Member
edited December 2018 in Product Feedback - Resolved

I am trying to perform topic modelling , it would be great to have a native LDA (latent dirichlet allocation) operator and also a Gradient Boost Operator.

4 votes

Fixed and Released · Last Updated


  • bhupendra_patilbhupendra_patil Administrator, Employee, Member Posts: 168  RM Data Scientist

    rapidminer 7.2 added Gradient Boosted Trees as a new learner


    Quoted from the operator help 


    A gradient boosted model is an ensemble of either regression or classification tree models. Both are forward-learning ensemble methods that obtain predictive results through gradually improved estimations. Boosting is a flexible nonlinear regression procedure that helps improving the accuracy of trees. By sequentially applying weak classification algorithms to the incrementally changed data, a series of decision trees are created that produce an ensemble of weak prediction models. While boosting trees increases their accuracy, it also decreases speed and human interpretability. The gradient boosting method generalizes tree boosting to minimize these issues

  • djeastmdjeastm Member Posts: 1 Contributor I

    There is an LDA operator in the KobRA-Projekt extension at The documentation is in German.

  • IrisSIrisS Member Posts: 2 Contributor I

    There's a corpus linguistics plugin 1.1.1 in Marketplace that should offer LDA functionality.

  • sgenzersgenzer 12Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959  Community Manager

    LDA is an extension but not updated (see here); Gradient Boosted Trees released in RM 7.2.

  • tftemmetftemme Administrator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, RMResearcher, Member Posts: 162  RM Research
    LDA is in the Operator Toolbox Extension (Operators: Extract Topics from Documents (LDA); Extract Topics from Data (LDA). Ticket can be closed.
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