A linear programming approach for optimizing features in ML models

This post introduces a linear programming approach to optimize feature selection for ML models, particularly for ranking algorithms like those used in News Feed and ad delivery. It addresses the challenge of balancing model performance gains from new features against finite infrastructure capacity (memory, CPU, storage). The post details how framing this as a linear programming problem allows for maximizing model performance, probing sensitivity to infrastructure constraints, and quantifying trade-offs, thereby automating and scaling the feature approval process.