Hey Andy, sorry been busy all day. You mean something like this to make it
more clear ?
>>> kernel_param = {'kernel':('linear', 'rbf')}
>>> C_param = {'C':[1,10]}
>>> parameters = (kernel_param, C_param) #List of parameter dictionaries
Sorry I'm a bit scatter brained with my visa appointment coming up :]
2013/3/21 Andreas Mueller <amuel...@ais.uni-bonn.de>
> On 03/21/2013 08:26 PM, Jaques Grobler wrote:
>
> Looks like he's referring to:
> http://scikit-learn.org/dev/modules/generated/sklearn.grid_search.GridSearchCV.html
>
> Examples
>
> >>> from sklearn import svm, grid_search, datasets>>> iris =
> >>> datasets.load_iris()>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1,
> >>> 10]}>>> svr = svm.SVC()>>> clf = grid_search.GridSearchCV(svr,
> >>> parameters)>>> clf.fit(iris.data, iris.target)...
> >>> GridSearchCV(cv=None, estimator=SVC(C=1.0, cache_size=...,
> >>> coef0=..., degree=..., gamma=..., kernel='rbf', max_iter=-1,
> >>> probability=False, shrinking=True, tol=...), fit_params={},
> >>> iid=True, loss_func=None, n_jobs=1, param_grid=..., ...)
>
>
> Btw,
>
> >>> parameters = {'kernel':('rbf',), 'C':[1, 10]}
>
> works ;)
>
>
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