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With such imbalanced data, the area under the ROC curve is not really informative. Area under the precision recall curve is better.


The positive/negative distinction is not what the precision/recall pair of measures tries to capture. precision measures the proportion of correctly predicted instances among the instances predicted as positive. In other words, if X is the precision then one can say "when the classifier predicts an instance as positive, it is correct X% of the time&...


I don't think evaluating clustering output labels using classification makes sense. As if K means has created those cluster mostly they will be separable on the input classes. To Validate your clustering model you should be doing the following. Look at Silhoutte Analysis for cluster = 7, and see if its well seprated Do the profiling of all the variables to ...

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