I 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 implem

I 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...