Lazy Learning in machine learning is a learning method in which generalization beyond 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. Lazy learning is essentially an instance-based learning: it simply stores training data (or only minor processing) and waits until it is given a test tuple. 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. 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.
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