Intermediate · Data

Feature Selection

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Choosing the most useful subset of features to improve performance and interpretability.

Technical Definition

Choosing the most useful subset of features to improve performance and interpretability.

How it works

Fewer features mean faster models, less overfitting, and easier explanations. Methods include filter (correlation, chi-squared), wrapper (recursive feature elimination), and embedded (L1 regularization, tree feature importances). Crucial in regulated domains where every input must be justified.

Related Concepts

• Regularization — Techniques that constrain a model's complexity to prevent overfitting and improve generalization to unseen data.
• Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
• Explainability (XAI) — Techniques making AI decisions understandable to humans, crucial for trust and regulatory compliance.