2 ms·
I'm not sure I agree:i I'll say it differently: As far as I know, k-means (as the author described it) has two parameters: initial number of clusters K and max
by misiti3780 13y ago
I'm not sure I agree:i I'll say it differently:
As far as I know, k-means (as the author described it) has two parameters: initial number of clusters K and max iterations (although I could be wrong), SVMS have:
UPDATE: here is a gist:
https://gist.github.com/josephmisiti/7572696 https://gist.github.com/josephmisiti/7572696
taken from here : http://www.csie.ntu.edu.tw/~cjlin/libsvm/ http://www.csie.ntu.edu.tw/~cjlin/libsvm/
I do not think that picking up LibSVM, compiling it, and running it is straight forward for everyone ...
- iskander 13y agoI think there's a big difference between explaining the essence of an algorithm and understanding all the details and decisions that go into a particular implementation. If explaining SVM, I'd stick with the linear primal formulation, which only really requires choosing the slack parameter C. If I needed to perform or explain non-linear prediction, I'd switch to the RBF kernel, which gives you one additional parameter (the RBF variance). Implementations of K-means, by the way, can also potentially have lots of parameters. Check out: http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html#sklearn.cluster.MiniBatchKMeans http://scikit-learn.org/stable/modules/generated/sklearn.clu...
- misiti3780 13y agofair enough - btw i just realized i bookmarked your blog (http://blog.explainmydata.com http://blog.explainmydata.com) a few weeks back - i was really enjoying those posts. nice work
- iskander 13y agoThanks :-) I'm going to finish grad school soon and the job I'm going to will be pretty rich with data analysis, hopefully will lead to more blog posts.