Question

what is a major drawback to the basic majority voting classification in knn? it requires frequent human subjective input during computation. classes that are more clustered tend to dominate prediction. even the naive version of the algorithm is hard to implement. classes with more frequent examples tend to dominate prediction.

Answer 1

Major drawback is examples from a more basic unit typically dominate predictions for new samples because, given their abundance, they seem to be prevalent among the k close neighbours.

Describe KNN.

Algorithm of K Nearest Neighbours. The k-nearest neighbours algorithm, sometimes referred to as KNN or k-NN, is a supervised learning classifier that employs proximity to produce classifiers or predictions about the clustering of a single data point.

What is KNN used for?

Because it produces extremely accurate predictions, the K - nn algorithm may compete against the most accurate models. As a result, the KNN method can be used for applications that need great accuracy but don't need a model that can be read by humans. The distance measurement affects how accurate the projections are.

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