What are the most effective feature engineering techniques for improving tabular data model accuracy?

All answers to this question.

For high-cardinality categorical features, I’ve found that Target Encoding or Leave-One-Out Encoding works much better than simple One-Hot Encoding, which often leads to the "curse of dimensionality." In my experience during a 2023 retail project, we saw a 5% jump in AUC just by switching to CatBoost, which handles these categories natively. For timestamps, don't just use the raw date; decompose them into "is_weekend," "hour_of_day," or "days_since_last_event." These cyclical features provide the temporal context the model needs to find actual behavioral patterns rather than just memorizing dates.

   Answered 2025-05-16 by Cynthia Reynolds

Have you tried using a SHAP analysis to see which of your current features are actually contributing to the model's decisions?

   Answered 2025-05-18 by Jeffrey Hudson

• That is an excellent suggestion, Jeffrey. SHAP values are a lifesaver when you're stuck in the workflow. They don't just tell you which features are important; they show you how each feature pushes the prediction in a certain direction. We used SHAP last year to realize that a feature we thought was "noise" was actually the primary driver for our top-tier customers. It allowed us to double down on that specific data collection and ignore 20 other irrelevant variables that were just slowing down our training.

     Commented 2025-05-19 by Gregory Marshall

Try interaction features. Sometimes the relationship between "Price" and "User Age" is more important than either one alone for prediction.

   Answered 2025-05-20 by Bradley Fischer

• I totally agree with Bradley. Polynomial features or simple ratios between columns can often reveal non-linear relationships that the model struggles to find on its own.

     Commented 2025-05-21 by Cynthia Reynolds