Comment Comment by windsignaling I'm surprised no one else has commented that a few of the conceptual comments in this article are a bit odd or just wrong.> The final accuracy is 90% because 1 of the 10 observations is on the incorrect side of the decision boundary.Who is using K-means for classification? If you have labels, then a supervised algorithm seems like a more appropriate choice.> K-means clustering is a recursive algorithmIt is?> If we know that the distributions are Gaussian, which is very frequently the case in machine learningIt is?> we can employ a more powerful algorithm: Expectation Maximization (EM)K-means is already an instance of the EM algorithm. parent windsignaling 1 year ago
> The final accuracy is 90% because 1 of the 10 observations is on the incorrect side of the decision boundary.
Who is using K-means for classification? If you have labels, then a supervised algorithm seems like a more appropriate choice.
> K-means clustering is a recursive algorithm
It is?
> If we know that the distributions are Gaussian, which is very frequently the case in machine learning
It is?
> we can employ a more powerful algorithm: Expectation Maximization (EM)
K-means is already an instance of the EM algorithm.