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:
parameters = {
> 'C' : np.arange(1, 100+1, 1).tolist(),
> 'kernel' : ['linear', 'rbf'],#precomputed,'poly',, 'sigmoid'
> 'degree' : np.arange(0, 100+0, 1).tolist(),
> 'gamma' : np.arange(0.0, 10.0+0.0, 0.1).tolist(),
> 'coef0' : np.arange(0.0, 10.0+0.0, 0.1).tolist(),
> 'shrinking' : [True],
> 'probability' : [False],
> 'tol' : np.arange(0.001, 0.01+0.001, 0.001).tolist(),
> 'cache_size' : [2000],
> 'class_weight' : [None],
> 'verbose' : [False],
> 'max_iter' : [-1],
> 'random_state' : [None],
> }
>
> model = grid_search.RandomizedSearchCV(n_iter=500, estimator=svm.SVC(),
> param_distributions=parameters,
> n_jobs=4,iid=True,refit=True,cv=5,verbose=1,pre_dispatch='2*n_jobs')#scoring='accuracy'
> model.fit(train_X, train_Y)
> print(model.best_estimator_)
> print(model.best_score_)
> print(model.best_params_)
>
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