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Can you explain your thought process for determining the appropriate value for "k" when implementing the K-means algorithm?

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Behavioral
  • What is your strategy for picking the best number of clusters for the K-means algorithm.
  • Could you elaborate on your methodology of evaluating the number of clusters needed in the K-means algorithm?
  • How would you approach the selection of the number of clusters in K-means, given a dataset with multiple variables?
  • What techniques do you use when determining the optimum value of "k" in the K-means algorithm?
  • What methods have you found useful for determining the most appropriate value of "k" for a given dataset using the K-means algorithm?
  • What's your approach to choosing the right number of clusters in K-means, and how do you ensure the optimal balance between extremes?
  • What methodology would you follow when implementing the K-means algorithm to decide on the best number of clusters?
  • How would you select the value of "k" in the K-means algorithm?
  • Can you explain your thought process for determining the appropriate value for "k" when implementing the K-means algorithm?
  • How do you go about choosing the optimal number of clusters for K-means algorithm without overfitting?
  • What factors do you consider when selecting the value of "k" in the K-means algorithm?

Interview question asked to Machine Learning Engineers interviewing at Calm, Amazon, Pinterest and others: Can you explain your thought process for determining the appropriate value for "k" when implementing the K-means algorithm?.