On 03/14/2012 10:08 PM, Andreas wrote: > Like Emanuel said, the full grid is already available. > If someone really cares about their parameter selection, they should > have a look > and decide themselves. > > I think 'best_estimator' should be chosen in the easiest way possible. > > I agree with David, that this should not happen in practice and so I think > it is of little relevance. I disagree on returning a list, though, > since that would make the usage a bit more awkward and will > be of use only in some degenerate cases. > > I am a bit against random choice as that would make the > GridSearch have a random_state, which is a bit unexpected. > If there really was a case where there were multiple optimum > settings on the training set - say k in KNN, then the result > on the test-set would be non-deterministic. > That might be a bit unexpected, if you are using a deterministic > classifier like KNN. > > On the other hand, I really think this is of little practical relevance. > And if you care so little about your results that you don't look > at what your grid search gave you, then you shouldn't care > about which of the 'optimum' parameters you got. > > just my .02 euro > By the way, there should be a utility function to visualize the result of the grid search. I always write the same couple of lines for unpacking the dict into a matrix / plot and reshaping to get the axis right.
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