2014-10-13 10:37 GMT+02:00 Adamantios Corais <adamantios.cor...@gmail.com>:
> I am running into the problem that the hyperparameters of my svm.SVC() are
> too wide such that the GridSearchCV() never gets completed! One idea is to
> use RandomizedSearchCV() instead. But again, my dataset is relative big such
> that 500 iterations take about 1 hour! My question is, what is a good set-up
> (in terms of the range of values for each hyperparameter) in GridSearchCV
> (or RandomizedSearchCV) in order to stop wasting resources... In other
> words, how to decide whether or not e.g. C values above 100 make sense
> and/or step of 1 is neither big not small? Any help is very much
> appreciated. This is the set-up am currently using:
Start off by not grid-searching tol. It determines when to stop
learning, not what the model should look like. You're probably fitting
practically the same set of models ten times by using many similar
values for tol.
Second, use per-kernel grids to prevent searching along irrelevant dimensions:
parameters = [
{'kernel': ['linear'], 'C': C_values, ...}
{'kernel': ['rbf'], 'C': C_values, 'gamma': gamma_values, ...}
]
The linear kernel ignores degree, gamma and coef0. The RBF kernel
ignores degree and coef0. GridSearchCV doesn't know this, so you're
again fitting the same models many times.
As for the actual question: C is usually determined by first using a
coarse, exponential range like 'C': [1, 10, 100, 1000]. You can then
"zoom in" on the optimum in a second grid-search (if it was 10, try
[5, 20, 50]).
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