Feature selection is a key step in building powerful and interpretable machine learning models, but it’s also one of the easiest to get wrong.
The wrong features will give you inaccurate answers and may impact your ML models’ efficiency in ways you can’t predict.
Focusing on establishing a reliable feature selection process will pay dividends when you move your models into production and have less margin for error in your predictions. More importantly, prioritizing feature stability and explainability will make your models more future-proof.
This guide breaks down: