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Eager learning
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A machine learning approach where the model builds a general hypothesis during training, applying it to all future predictions.
Technical Definition
A machine learning approach where the model builds a general hypothesis during training, applying it to all future predictions.
How it works
Eager learning, in contrast to lazy learning, requires the learning algorithm to induce a general model from the entire training data before any queries are made. Once trained, the model is ready to classify new instances without further learning. Decision trees and naive Bayes classifiers are examples of eager learners.
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.
- Lazy learning — Lazy learning is a machine learning approach where model generalization is deferred until a query is made, contrasting with eager learning.