Lazy learning

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In machine learning, lazy learning is a learning method in which generalization of the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.

The main advantage gained in employing a lazy learning method, such as case-based reasoning, is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes in the problem domain.

Lazy learning refers to any machine learning process that defers the majority of computation to consultation time. Two typical examples of lazy learning are instance-based learning and Lazy Bayesian Rules. Lazy learning stands in contrast to eager learning in which the majority of computation occurs at training time.

The disadvantages with lazy learning include the large space requirement to store the entire training dataset. Particularly noisy training data increases the case base unnecessarily, because no abstraction is made during the training phase. Another disadvantage is that lazy learning methods are usually slower to evaluate, though this is coupled with a faster training phase.

Lazy classifiers are most useful for large datasets with few attributes.

References[edit]

  • lazy: Lazy Learning for Local Regression, R package with reference manual
  • "The Lazy Learning Package". Archived from the original on 16 February 2012.
  • Webb G.I. (2011) Lazy Learning. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA