State the reason behind categorising a learning technique as eager or lazy
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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.
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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. ... Lazy learning stands in contrast to eager learning in which the majority of computation occurs at training time.
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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. ... Lazy learning stands in contrast to eager learning in which the majority of computation occurs at training time.
Hope it will help you ☺❤
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