Designing Machine Learning Systems | Chapter 5: Feature Engineering

onepagecode · Beginner ·📐 ML Fundamentals ·3d ago
Skills: ML Pipelines53%

About this lesson

Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures In Chapter 5 of "Designing Machine Learning Systems" by Chip Huyen, we explore one of the most impactful areas in building high-performing ML systems: feature engineering. This chapter explains why having the right features often gives a bigger performance boost than complex model architectures or hyperparameter tuning. We start by comparing learned features (from deep learning) versus engineered features, and why most real-world ML systems still require significant manual feature engineering. We then cover essential feature engineering techniques including handling missing values (MNAR, MAR, MCAR), feature scaling, discretization, encoding categorical variables (including the powerful hashing trick), feature crossing, and positional embeddings (both discrete and continuous/Fourier features). A major focus of this chapter is data leakage — one of the most dangerous and common problems in production ML. We discuss multiple causes of data leakage (time-based splitting issues, scaling before splitting, poor handling of duplicates, group leakage, and leakage from the data generation process) along with practical ways to detect and prevent it. Finally, we discuss how to engineer good features by balancing feature importance and generalization, and when to remove features that no longer add value. What you’ll learn in this chapter: • Learned features vs engineered features in modern ML • Handling missing values properly (deletion vs imputation) • Feature scaling and log transformation techniques • Encoding high-cardinality categorical features using the hashing trick • Feature crossing for nonlinear relationships • Positional embeddings and Fourier features • Common causes of data leakage and how to detect them • Measuring feature importance and generalization • Best practices for maintaining features in production T

Original Description

Read the detailed version on: https://onepagecode.substack.com/ Use this url: https://onepagecode.substack.com/p/large-language-models-architectures In Chapter 5 of "Designing Machine Learning Systems" by Chip Huyen, we explore one of the most impactful areas in building high-performing ML systems: feature engineering. This chapter explains why having the right features often gives a bigger performance boost than complex model architectures or hyperparameter tuning. We start by comparing learned features (from deep learning) versus engineered features, and why most real-world ML systems still require significant manual feature engineering. We then cover essential feature engineering techniques including handling missing values (MNAR, MAR, MCAR), feature scaling, discretization, encoding categorical variables (including the powerful hashing trick), feature crossing, and positional embeddings (both discrete and continuous/Fourier features). A major focus of this chapter is data leakage — one of the most dangerous and common problems in production ML. We discuss multiple causes of data leakage (time-based splitting issues, scaling before splitting, poor handling of duplicates, group leakage, and leakage from the data generation process) along with practical ways to detect and prevent it. Finally, we discuss how to engineer good features by balancing feature importance and generalization, and when to remove features that no longer add value. What you’ll learn in this chapter: • Learned features vs engineered features in modern ML • Handling missing values properly (deletion vs imputation) • Feature scaling and log transformation techniques • Encoding high-cardinality categorical features using the hashing trick • Feature crossing for nonlinear relationships • Positional embeddings and Fourier features • Common causes of data leakage and how to detect them • Measuring feature importance and generalization • Best practices for maintaining features in production T
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