Intermediate · Fundamentals
Empirical risk minimization (ERM)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A machine learning principle that aims to find a model that performs best on the observed training data.
Technical Definition
A machine learning principle that aims to find a model that performs best on the observed training data.
How it works
Empirical Risk Minimization (ERM) is a common learning strategy where the goal is to select a model that minimizes the error or loss calculated on the training dataset. This approach assumes that a model performing well on the training data will generalize well to unseen data. However, it can sometimes lead to overfitting if not balanced with regularization.
Related Concepts
- Loss Function — A mathematical function that measures how far the model's predictions are from the actual values, guiding the learning process.
- Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
- Generalization — A model's ability to perform well on new, unseen data — not just its training set.