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04_​Topic_​Modeling_​LDA_​K_​Optimization

Topic Modeling with LDA: Optimizing K via a Verified Component

The Topic Scorer (Labs) verified component implements an experimental score for semantic coherence, exclusivity and similarity/distance of topics of one or multiple models.

As K increases trends can be found using these scores and a decision for the optimal K can be used. Usually it is a tradeoff between those metrics. Read more on the Verified Components descriptions.

Topic Modeling with LDA: Optimizing K via a Verified Component The Topic Scorer (Labs) verified component implements an experimental score for semantic coherence, exclusivity and similarity/distance of topics of one or multiple models. As K increases trends can be found using these scores and a decision for the optimal K can be used. Usually it is a tradeoff between those metrics. Read more on the Verified Components descriptions. TripAdvisor hotels reviewsright click to open chartskadd model idPerplexity Strings To Document Excel Reader View Exclusivityand Coherence by K Parameter OptimizationLoop Start Loop End GroupBy ConstantValue Column DocumentPreprocessing Topic Extractor(Parallel LDA) Topic Scorer (Labs) ConstantValue Column Math Formula Top k Selector Column Appender Topic Modeling with LDA: Optimizing K via a Verified Component The Topic Scorer (Labs) verified component implements an experimental score for semantic coherence, exclusivity and similarity/distance of topics of one or multiple models. As K increases trends can be found using these scores and a decision for the optimal K can be used. Usually it is a tradeoff between those metrics. Read more on the Verified Components descriptions. TripAdvisor hotels reviewsright click to open chartskadd model idPerplexity Strings To Document Excel Reader View Exclusivityand Coherence by K Parameter OptimizationLoop Start Loop End GroupBy ConstantValue Column DocumentPreprocessing Topic Extractor(Parallel LDA) Topic Scorer (Labs) ConstantValue Column Math Formula Top k Selector Column Appender

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