Question

What is instance based learning in machine learning?

Answer 1

Answer:

In machine learning, instance-based learning (sometimes called memory-based learning[1]) is a family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory.

Explanation:

It is called instance-based because it constructs hypotheses directly from the training instances themselves.[2] This means that the hypothesis complexity can grow with the data:[2] in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away.

Answer 2

In machine learning, ins. tance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem ins.tances with in.stances seen in training, which have been stored in memory.

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