Hello everyone,
I'm trying to execute a grid search with the GridSearchCV class (for an
AdaBoostClassifier) and want to use a custom sample_weight vector to
begin with. However, I can't figure out, how to do this. Passing the
parameter to GridSearchCV's fit()-method gives me the message, that this
is deprecated and will be ignored. Passing it to the
GridSearch-constructor like so:
grid = GridSearchCV(clf, params, fit_params={'sample_weights': my_weights})
doesn't work either, because it tries to apply the same weights vector
to each of the different folds, which of course doesn't make sense and
fails because the size of my_weights doesn't correspond to the sizes of
the folds. Can I somehow specify a weights vector for each fold? Is
there an easier way? Is this even possible?
I'm sorry, if I'm missing something obvious here.... By the way, I'm
using the latest development version (0.14-git).
Best regards.
Peter
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