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||VARBYTE(n)||Model in binary format, which is not readable. To see binary contents, use LDATopicSummary function.
Column size depends on InputTable size and may be very large. If any cell of this column exceeds 64 KB, you cannot use ModelTable with LDAInference or LDATopicSummary function.
This table appears only with the OutputTable argument.
|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 argument.] Topic weight.|
|topicwords||VARCHAR||[Column appears number of times specified by OutputTopicWordNum argument.] Topic words in document, separated by commas.|