COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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K-means is supervised while KNN is unsupervised
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K-Means is used for clustering while KNN is used for classification and regression
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Either A or B
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None of the above
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Detailed explanation-1: -k-means clustering is a form of unsupervised learning, which means that it does not require training data. k-NN clustering, on the other hand, is a form of supervised learning, which means that it does require training data. k-means clustering is also a much faster algorithm than k-NN clustering.
Detailed explanation-2: -KNN is a supervised learning algorithm mainly used for classification problems, whereas K-Means (aka K-means clustering) is an unsupervised learning algorithm. K in K-Means refers to the number of clusters, whereas K in KNN is the number of nearest neighbors (based on the chosen distance metric).
Detailed explanation-3: -K-NN is a Supervised machine learning while K-means is an unsupervised machine learning. K-NN is a classification or regression machine learning algorithm while K-means is a clustering machine learning algorithm. K-NN is a lazy learner while K-Means is an eager learner.
Detailed explanation-4: -The key differences are: KNN regression tries to predict the value of the output variable by using a local average. KNN classification attempts to predict the class to which the output variable belong by computing the local probability.
Detailed explanation-5: -K-means algorithm generally assumes that the clusters are spherical or round i.e. within k-radius from the cluster centroid. In K means, many iterations are required to determine the cluster centroid. In spectral, the clusters do not follow a fixed shape or pattern.