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How do k-means clustering and k-nearest neighbors algorithm differ in machine learning?

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Behavioral
  • How do k-means clustering and k-nearest neighbors algorithm differ in machine learning?
  • Can you distinguish between the k-means and KNN techniques?
  • What contrasts exist between the k-means algorithm and the k-nearest neighbors method?
  • Could you explain the disparities between k-means clustering and the k-nearest neighbor algorithm?
  • How are k-means and KNN distinct from each other in terms of their applications?
  • What is the fundamental difference between the k-means clustering approach and k-nearest neighbors?
  • What separates k-means as a clustering method from k-nearest neighbors as a classification method?
  • Can you differentiate the purposes of k-means clustering versus k-nearest neighbor techniques?
  • In what way does the k-means algorithm function differently from the k-nearest neighbors algorithm?
  • How do the objectives of k-means and k-nearest neighbors differ in data analysis?
  • What is the difference between k-means and k nearest neighbor (KNN).

Interview question asked to Machine Learning Engineers interviewing at Huawei, Evernote, Disney and others: How do k-means clustering and k-nearest neighbors algorithm differ in machine learning?.