Intermediate · Evaluation
Generalization error
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A measure of how poorly a machine learning model performs on new, unseen data compared to its training data.
Technical Definition
A measure of how poorly a machine learning model performs on new, unseen data compared to its training data.
How it works
Generalization error quantifies a machine learning model's inability to perform well on data it hasn't encountered during training. Minimizing this error is crucial for building models that are reliable and effective in real-world applications.
Related Concepts
- Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Underfitting — A phenomenon where a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and new data.