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Usually you should develop multiple models simultaneously. As the No Free Lunch Theorem states there is no way to know which model will perform better, before modeling. In practice you can make some educated guesses, but there is no need to rush them. If your output is continuous you shouldn't use a classification model like logistic regression. Although the ...


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Assuming you mean sklearn.cluster.KMeans, you are able to pass in the initialization points using the init argument: init : {‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’ Method for initialization: ‘k-means++’ : selects initial cluster centers for k-mean clustering in a smart way to speed up ...


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The intuition is to choose a point that is as far as possible from the existing centers. It does not matter in which direction the new point lies, as long as it is far away.


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I think the k-prototype algorithm is what you are looking for. https://link.springer.com/article/10.1023/A:1009769707641


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