Output Message Schema
|message||VARCHAR||Reports iteration steps and perplexity of model.
perplexity = 2 H (p) = 2-Σ x p (x) log2 p (x)
where H (p) is the entropy of the distribution.
Although perplexity varies with training documents, you can use perplexity to find the best model for a specified set of training documents: Create models for several subsets of the training documents and then choose the model with the lowest perplexity.
|topicid||INTEGER||Internally created topic identifier.|
|value_col||BLOB||Model in binary format, which is not readable. To see binary contents, use LDATopicSummary (ML Engine) function.|
This table appears only with the OutputTable syntax element.
|docid||Same as doc_id_column in input table||Document identifier from input table.|
|topicid||INTEGER||Topic identifier from ModelTable.|
|topicweight||DOUBLE PRECISION||[Column appears number of times specified by OutputTopicNum syntax element.] Topic weight.|
|topicwords||VARCHAR||[Column appears number of times specified by OutputTopicWordNum syntax element.] Topic words in document, separated by commas.|