Explain eager decision tree algorithm-lazy knn algorithm

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Reference no: EM1364742

Suggest a lazy version of the eager decision tree algorithm by describing the main idea of the new algorithm

Question

An eager learner (e.g., decision tree) builds a fixed and global model based on the training data and uses the same global model for each test instance, while a lazy learner (e.g., k-nearest neighbor) builds a flexible and local model based on the training data for each test instance. First, suggest a lazy version of the eager decision tree algorithm by describing the main idea of the new algorithm, and then discuss the advantages and disadvantages of the new algorithm compared with the eager decision tree algorithm, and the advantages and disadvantages of the new algorithm compared with the lazy kNN algorithm.

Reference no: EM1364742

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