I've spent weeks tuning a Deep Learning model for customer lifetime value prediction with mediocre results. A colleague suggested that I should stop focusing on the architecture and spend more time on feature engineering, like creating "Recency, Frequency, Monetary" (RFM) variables. In your experience, does better feature engineering usually beat a more "sophisticated" algorithm?
3 answers
In the world of tabular predictive analytics, the "Garbage In, Garbage Out" rule reigns supreme. You can have the most complex Neural Network, but if your features don't capture the underlying signal, it will fail. Domain-specific features like RFM, rolling averages, or debt-to-income ratios often provide the "lift" that no amount of hyperparameter tuning can achieve. Most Kaggle winners spend 80% of their time on feature engineering and only 20% on the model. Better features allow simpler, more interpretable models like Logistic Regression to perform as well as complex ones.
Have you tried using Automated Feature Engineering tools like Featuretools? Sometimes they find interactions between variables that a human might miss during manual EDA.
Definitely focus on the data. I've seen a simple Linear Regression with great features beat a poorly tuned XGBoost any day of the week.
Spot on, Dorothy. Simplicity in the model combined with richness in the features is the secret sauce for most production-grade predictive systems.
Featuretools is great, but be careful with "Data Leakage." If you create features using information from the future (like total future spend), your model will look perfect in training but fail in the real world. Always ensure your feature engineering happens within a cross-validation loop and only uses information that would be available at the exact moment the prediction is made.