Intermediate · Data
Lazy learning
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. Lazy learning is a machine learning approach where model generalization is deferred until a query is made, contrasting with eager learning.
Technical Definition
Lazy learning is a machine learning approach where model generalization is deferred until a query is made, contrasting with eager learning.
How it works
Lazy learning methods, such as k-nearest neighbors, delay the process of building a general model until after a query is received. The training data is stored, and specific computations are performed only when a prediction is needed. This contrasts with 'eager' learners, which build a model during training.
Related Concepts
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Supervised Learning — Learning from input–output pairs where each training example carries a correct label.
- Eager learning — A machine learning approach where the model builds a general hypothesis during training, applying it to all future predictions.
- K-nearest neighbors — K-Nearest Neighbors (KNN) is a supervised learning algorithm used for classification and regression that predicts a data point's class based on its nearest neighbors.