AI engineering glossary · Classical ML algorithms
What is lazy learning?
Lazy learning (also called Instance-based learning): An approach where a model postpones work until a prediction is requested, keeping the training data and comparing against it each time, rather than building a summary in advance. Neighbour-based methods are the standard example.
Lazy learning: an example
A neighbours model 'trains' in milliseconds by storing 1 million rows, but each prediction must search them.
Related terms
- k-nearest neighbours
- Eager learning
- KD-tree
More Classical ML algorithms terms
- Lift
- LightGBM
- Linear discriminant analysis
- Linear regression
- Linkage criterion
- Local outlier factor
- Log-odds
- Logistic regression
Where you learn it
Lazy learning is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.