See the distance.pdist function for a list of valid distance metrics. The distance metric to use in the case that y is a collection of observation vectors ignored otherwise. See the Linkage Methods section below for full descriptions. Alternatively, a collection of mm observation vectors in n dimensions may be passed as an mm by nn array. A condensed distance matrix is a flat array containing the upper triangular of the distance matrix. (y, method=’single’, metric=’euclidean’)Ī condensed or redundant distance matrix. What format does our data have to be in to be able to run on it? That all seems nice and easy, right? (It wasn’t.) Or we can plot it so that the same colors are generated on each plot, and just look at it visually to determine how well we did. accuracy, precision, recall, f1-score etc.) to see how well our clustering model did. With this, we can use classification metrics (e.g. unique ( cut ): mask = ( cut = i ) labs = stats.
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