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Compare the advantages and disadvantages of eager classification versus lazy classification?
Wiki User
∙ 14y ago
Eager classification involves building a model during the training phase, allowing for faster predictions during the testing phase but making it less flexible to changes in the dataset. In contrast, lazy classification postpones the model building until prediction time, which allows for greater adaptability to changes in the dataset but can lead to slower prediction times. Eager classification typically requires more memory and computational resources during training compared to lazy classification.
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