Using the sklearn grid_search tools

Scikit-learn has a really nice grid search module. It will soon be called model_selection, because it has grown beyond simple grid search. But here is the spirit of it:

import sklearn.svm, sklearn.grid_search, sklearn.datasets.samples_generator
parameters = {'kernel':('poly', 'rbf'), 'C':[.01, .1, 1, 10, 100]}
clf = sklearn.grid_search.GridSearchCV(
    sklearn.svm.SVC(probability=True),
    parameters,
    n_jobs=64)
X, y = sklearn.datasets.samples_generator.make_classification(n_samples=200, n_features=5, random_state=12345)
clf.fit(X, y)
clf.best_params_

And say you want to take a careful look at the results? They are all in there, too. http://nbviewer.ipython.org/gist/aflaxman/cb0660e602d361d06599