Output Message Schema
| Column | Data Type | Description |
|---|---|---|
| message | VARCHAR | Reports iteration steps and perplexity of model. Perplexity formula: perplexity = 2H (p) = 2-Σx 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. |
ModelTable Schema
| Column | Data Type | Description |
|---|---|---|
| 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. |
OutputTable Schema
This table appears only with the OutputTable syntax element.
| Column | Data Type | Description |
|---|---|---|
| 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. |