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 blacklist 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