Feature Selection

The Feature Selection algorithm enables Coveo Machine Learning (Coveo ML) models to automatically filter and refine which context keys to use or ignore when building ML models. The Feature Selection algorithm uses a wide variety of factors to determine whether to use a dimension to personalize suggestions or recommendations.

Examples of tests which the Feature Selection model carries out to determine whether to blocklist a context key:

  • Check whether the data contains many long strings of text which might not be intended/suitable for classification

  • Check whether the data is empty

  • Check whether the data contains too many unique values

  • Check whether the data contains only a single value

  • Check whether the data is considered irrelevant according to some ML/Statistical tests

It’s possible to override the Feature Selection algorithm to forcefully use or ignore custom context keys by using the whitelist and blacklist parameters (see Custom Model Parameters).

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