Summary

• Instance-based learners delay processing until prediction time
• they form a different local approximation for each query instance
• can model complex functions by a combination of less-complex local approximations
• information present in the training data is never lost (is this a benefit!!!)
• computationally expense to label new instances
• finding appropriate distance metric can be difficult and negative impact of irrelevant attributes