Hi Satra,
In my experience, adjusting max_features can make some difference (I work with
image data).
Cheers,
Michal
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thanks andy.
are there any general heuristics for these parameters - given that their
ranges are over the samples?
max_depth = range(1, nsamples)
or
min_samples_leaves = range(1, nsamples)
also related question: given that nsamples would actually depend on the cv
method of the GridSearchCV, is t
Since LinearSVC doesn't have predict_proba, one must use algorithm="SAMME",
the original AdaBoost which uses the output of "predict".
This is not exactly a linear combination because of the sign function but
still a linear SVM isn't really what I would use with Adaboost.
And it doesn't seem to impr
