scaling data makes sklearn svm slow
I'm doing some experiments with sklearn.svm.SVC with linear kernel.
I'm generating data like so
X[:50,:2] = (np.random.randn(50, 2)+[2,2])*scale
X[50:,:2] = (np.random.randn(50, 2))*scale
y = np.array([0]*50 + [1]*50)
I noticed that if I scale the data points by a factor 1000, then training takes much longer (in fact I have never seen it finish training). Why would scaling affect training time?
Actually when I scale it by just 10, it does finish training after a while but the accuracy is super low (it predicts everything to be the same thing). This almost seems like the SVM is not using bias or something. But I'm pretty sure it does...
scikit-learn svm
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
add a comment |
I'm doing some experiments with sklearn.svm.SVC with linear kernel.
I'm generating data like so
X[:50,:2] = (np.random.randn(50, 2)+[2,2])*scale
X[50:,:2] = (np.random.randn(50, 2))*scale
y = np.array([0]*50 + [1]*50)
I noticed that if I scale the data points by a factor 1000, then training takes much longer (in fact I have never seen it finish training). Why would scaling affect training time?
Actually when I scale it by just 10, it does finish training after a while but the accuracy is super low (it predicts everything to be the same thing). This almost seems like the SVM is not using bias or something. But I'm pretty sure it does...
scikit-learn svm
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52
add a comment |
I'm doing some experiments with sklearn.svm.SVC with linear kernel.
I'm generating data like so
X[:50,:2] = (np.random.randn(50, 2)+[2,2])*scale
X[50:,:2] = (np.random.randn(50, 2))*scale
y = np.array([0]*50 + [1]*50)
I noticed that if I scale the data points by a factor 1000, then training takes much longer (in fact I have never seen it finish training). Why would scaling affect training time?
Actually when I scale it by just 10, it does finish training after a while but the accuracy is super low (it predicts everything to be the same thing). This almost seems like the SVM is not using bias or something. But I'm pretty sure it does...
scikit-learn svm
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
I'm doing some experiments with sklearn.svm.SVC with linear kernel.
I'm generating data like so
X[:50,:2] = (np.random.randn(50, 2)+[2,2])*scale
X[50:,:2] = (np.random.randn(50, 2))*scale
y = np.array([0]*50 + [1]*50)
I noticed that if I scale the data points by a factor 1000, then training takes much longer (in fact I have never seen it finish training). Why would scaling affect training time?
Actually when I scale it by just 10, it does finish training after a while but the accuracy is super low (it predicts everything to be the same thing). This almost seems like the SVM is not using bias or something. But I'm pretty sure it does...
scikit-learn svm
scikit-learn svm
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
edited Dec 31 '18 at 23:19
Edmonds Karp
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
asked Dec 31 '18 at 23:09
Edmonds KarpEdmonds Karp
506
506
SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52
add a comment |
SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52
SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52
SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52
add a comment |
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SVMs work better with standardized data and linear svms are known to have a large time fitting. Do you absolutely need to work with Linear kernel?
– Vivek Kumar
Jan 2 at 9:52