πŸŽ“How I Study AIHISA
πŸ“–Read
πŸ“„PapersπŸ“°Blogs🎬Courses
πŸ’‘Learn
πŸ›€οΈPathsπŸ“šTopicsπŸ’‘Concepts🎴Shorts
🎯Practice
🧩Problems🎯Prompts🧠Review
Search

Concepts2

Category

πŸ”·Allβˆ‘Mathβš™οΈAlgoπŸ—‚οΈDSπŸ“šTheory

Level

AllBeginnerIntermediateAdvanced
Filtering by:
#cross validation
πŸ“šTheoryAdvanced

Statistical Learning Theory

Statistical learning theory explains why a model that fits training data can still predict well on unseen data by relating true risk to empirical risk plus a complexity term.

#statistical learning theory#empirical risk minimization#structural risk minimization+11
πŸ“šTheoryIntermediate

Bias-Variance Tradeoff

The bias–variance tradeoff explains how prediction error splits into bias squared, variance, and irreducible noise for squared loss.

#bias variance tradeoff#mse decomposition#polynomial regression+12