Feature Engineering: The ML Secret Weapon

Good features beat fancy algorithms. Here’s how to engineer features that actually matter.

Start with domain knowledge

What do you know about the problem? Use that knowledge to create features. A good feature engineer knows the domain better than the algorithm.

Sometimes the magic is in combinations. Age × income. Time of day × day of week. Let your features talk to each other.

One-hot encoding isn’t always the answer. Consider target encoding, embedding layers, or just keeping it simple with label encoding.

Time is gold in ML. Day of week, hour of day, time since last event. Temporal patterns are everywhere.

Instead of raw values, try ratios. Revenue per customer. Growth rate. Percentage changes. These often work better than absolute numbers.

Not all features are created equal. Remove the noise. Use correlation analysis, mutual information, or just good old-fashioned domain knowledge.

Sometimes yes/no is all you need. Is it a weekend? Is the user new? Is the price above average? Simple binary features often work wonders.

Will this feature be available when you make predictions? Don’t use features that won’t exist in production.

Need help with feature engineering? Let’s build better features.