I am reviewing some techniques based on scikit-learn and I would like to check what are the best parameters for SVM using Grid Search.
The thing is that I don't know how to use Grid Search to the code below although I have used it in the past successfully for simpler cases:
Obviously sparse matrix and Grid Search can't work together.
From your experience, what is the way to find the best parameters given what I have here?
clf=CalibratedClassifierCV(OneVsRestClassifier(SVC(C=1))) clf.fit(tr_data, df.type)
tr_data looks like this:
(0, 127887) 0.093453466346 (0, 13358) 0.276756756575 (0, 20712) 0.165645654645 (0, 13423) 0.076765456536 (0, 3653) 0.178745322453
df.type like this:
0 alt.atheism 1 alt.atheism 2 alt.atheism 3 alt.atheism
Tried something like this but has problem with the matrix I guess:
alphas = np.array([1,0.1,0.01,0.001,0.0001,0]) grid = grid_search.GridSearchCV(estimator = clf,param_grid = dict(alpha=alphas))
Any help will be much appreciated