difference between classification and regression in k-nearest neighbor

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what is the difference between using K-nearest neighbor in classification and using it in regression?

and when using KNN in recommendation system. Does it concerned as classification or as regression?
Mar 3, 2022 in Machine Learning by Nandini
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1 answer to this question.

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In classification tasks, the user tries to predict a category, which is commonly represented as an integer label but actually represents a group of "objects." You could, for example, use label 0 for "Rat" and 1 for "Cat" to categorize photographs into "Rat" and "Cat" categories.

The KNN classification algorithm will look at the k closest neighbors of the input you're trying to predict. The most common label among the k samples will then be output.

The user wishes to get a numerical value out of regression tasks (usually continuous). For example, it could be used to determine the value of a home or to rate the quality of a film.
In this situation, the KNN method would combine the values associated with the k nearest examples from the one on which you wish to make a prediction to produce a single value. Normally, the average of the neighbors' k values would be used, however the median or a weighted average might also be used (or actually anything that makes sense to you for the task at hand).

You could use both for your problem, but I think regression makes more sense for predicting some sort of "matching %"" between the user and the object you want to recommend to him.
Also K NN is a lazy learner as it learns via instances and also known as instance based learning.
Might become slow with large data set.
answered Mar 4, 2022 by Dev
• 6,000 points

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