Hi all,
I am working on an character recognition problem with the Chars74K data
set. I am reshaping the images to 30x30 pixels, and using the 900 pixels'
intensities as features. I am classifying the images using a SVC with an
RBF kernel.
...
pipeline = Pipeline([
('clf', SVC(kernel='rbf'))
])
parameters = {
'clf__gamma': (0.01, 0.03, 0.1, 0.3, 1),
'clf__C': (0.1, 0.3, 1, 3, 10, 30),
}
...
On CrunchBang 11 with scikit-learn 0.15.2, grid search yields the following
results:
Fitting 3 folds for each of 30 candidates, totalling 90 fits
[Parallel(n_jobs=3)]: Done 1 jobs | elapsed: 1.6min
[Parallel(n_jobs=3)]: Done 50 jobs | elapsed: 34.8min
[Parallel(n_jobs=3)]: Done 86 out of 90 | elapsed: 69.4min remaining:
3.2min
[Parallel(n_jobs=3)]: Done 90 out of 90 | elapsed: 71.6min finished
Best score: 0.559
Best parameters set:
clf__C: 3
clf__gamma: 0.03
precision recall f1-score support
001 0.00 0.00 0.00 6
002 1.00 0.20 0.33 5
...
061 0.00 0.00 0.00 4
062 0.00 0.00 0.00 4
avg / total 0.56 0.58 0.53 532
On Ubuntu 14.04 and OS X with scikit-learn 0.15.1 and 0.15.2, the same
model performs horribly. The following are the results of the script for
Ubuntu 14.04 with NumPy 1.8.2 and 0.14.0.
avg / total 0.09 0.07 0.02 532
Switching to a polynomial kernel on these platforms yields better
performance, but the RBF kernel still performs best.
It appears that the performance depends on the platform. What might be the
problem here?
Thanks,
Gavin
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