> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# CLASSIFY/RANK TOP K

#### `CLASSIFY | RANK TOP <K>` (Required for LIST\_DISTINCT)

## Description

A query with a `LIST_DISTINCT` aggregation target can serve two different targets: ranking or classification. The same applies to queries with multicategorical or multilabel target columns.

When predicting which products a user will buy, you are usually only interested in the ranking of the top few products that the user is most likely to buy. In that case, you can guarantee that by adding `RANK TOP K` at the end of your target definition, where `K` is the number of items that you are interested in.

On the other hand, you might be interested in a separate prediction for each item, making a separate binary prediction for each item type. To use this feature, add `CLASSIFY` to the end of your target definition.

### Example

The examples for this part of the query can be found here:

<CodeGroup>
  ```Text PQL theme={null}
  PREDICT LIST_DISTINCT(transaction.article_id, 0, 30) RANK TOP 12
  PREDICT LIST_DISTINCT(transaction.article_id, 0, 30) CLASSIFY
  PREDICT target.multicategorical_column RANK TOP 20
  ```
</CodeGroup>

The two operations are subject to different limits: ranking works up to `10,000,000` different entities, while classification only works with up to `1000` different entities. Ranking at most `1000` targets is permitted.

`TOP K` will be ignored if used with `CLASSIFY`. Adding `CLASSIFY`/`RANK` is required if the target output is `LIST_DISTINCT` or a multicategorical column.

`CLASSIFY`/`RANK` is not required and has no effect if `LIST_DISTINCT` appears as part of a condition, such as in the following pQuery:

<CodeGroup>
  ```Text PQL theme={null}
  PREDICT LIST_DISTINCT(transaction.category, 0, 30) CONTAINS "online"
  ```
</CodeGroup>

<Info>
  For predictions per entity value in batch predictions that differ from model training RANK TOP K value, Kumo uses the same trained model but produces the number of results specified at batch prediction time.
</Info>