AI engineering glossary · ML fundamentals
What is inductive bias?
Inductive bias: The set of assumptions a learning algorithm makes so that it can choose one answer over the many that fit the training data. Without such assumptions, no learning beyond memorising is possible. Examples include preferring smooth functions, or assuming nearby points share a label.
Inductive bias: an example
A nearest-neighbour method assumes points close together have similar labels; a convolutional network assumes patterns matter wherever they appear in an image.
Related terms
More ML fundamentals terms
- Inference
- Inter-annotator agreement
- Irreducible error
- k-fold cross-validation
- L1 regularisation
- L2 regularisation
- Label
- Label noise
Where you learn it
Inductive bias is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.