> ## 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.

# What columns should I select in a table?

For optimal results, you should ensure that any table columns you select for Kumo ingestion meet the following criteria:

* **Clean:** be sure to remove fake/synthetic data, predictions from other ML models, data for which the column definition has constantly changed over time (especially if a particular attribute ID may point to different things over time), and data that is known to be otherwise unreliable or frequently inaccurate.

* **Relevant and Mutually Exclusive:** the larger the graph size (i.e., the sum across the tables in a graph), the larger the compute cost; to optimize training costs, remove columns that provide similar/duplicated information, irrelevant information, and other extraneous data.

* **Complete:** the column should cover the full history across the timeframe in question (e.g., the whole record of purchases/interactions versus a user's first/last purchase, or a subscriber's most recent interaction). If this results in an oversized data set, you can provide Kumo with a compressed version that indicates changes in aggregate metrics over time (e.g., per day/week/month).

<Warning>
  Using the wrong or unnecessary columns can lead to both degraded model performance and increased training costs.
</Warning>