The difficulties with the k-nearest neighbor algorithm is option c. none of the above.
<h3>What problems does the K-nearest Neighbor algorithm have?</h3>
An unlabelled point is given a label based on its proximity to all other nearby labelled points. This is how it works. Its main drawbacks are that picking the "right" value of K is challenging and that it is highly computationally inefficient.
Hence, The supervised machine learning technique known as the k-nearest neighbors (KNN) can be used to tackle classification and regression issues. It is simple to use and comprehend, but it has the important problem of becoming noticeably slower as the amount of data in use increases.
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