Answer Posted / Surya Bhatia

Instance-based learning algorithms, also known as lazy learning algorithms, are called so because they postpone the training process until a query or prediction is made. Instead of building a model from the entire dataset like other learning methods, these algorithms store the data instances directly and retrieve them when needed for classification or regression tasks. This approach makes instance-based learning algorithms more flexible and adaptive to changing data distributions but can be computationally expensive.

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