Knnlk essay

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Why does KNN not work with high dimensional data?

Does not work well with high dimensions: The KNN algorithm doesn’t work well with high dimensional data because with large number of dimensions, it becomes difficult for the algorithm to calculate the distance in each dimension. 3.

How is KNN faster than other prediction algorithms?

In other words, there is no training period for it. It stores the training dataset and learns from it only at the time of making real time predictions. This makes the KNN algorithm much faster than other algorithms that require training e.g. SVM, Linear Regression etc. 2.

What are the advantages and disadvantages of kNN?

Advantages of KNN 1. No Training Period: KNN is called Lazy Learner (Instance based learning). It does not learn anything in the training period.

Is the kNN classifier easy to use?

KNN is very easy to implement. There are only two parameters required to implement KNN i.e. the value of K and the distance function (e.g. Euclidean or Manhattan etc.) 1.

Last Update: Oct 2021

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