: If substantial revision is required, re-examine the extraction step to create more complex "engineered" features.
: Check if the feature set evaluates performance accurately against known benchmarks.
: Apply mathematical functions (like log transforms or scaling) to normalize data.
Once you have a set of potential features, you must filter them to find the most "informative" ones to avoid "Big Data" noise and improve accuracy.