Why is my SVM taking so long?

The most likely explanation is that you’re using too many training examples for your SVM implementation. SVMs are based around a kernel function. Most implementations explicitly store this as an NxN matrix of distances between the training points to avoid computing entries over and over again.

Is SVM very slow?

linear_model. SGDClassifier. So we can do some math to approximate the time-difference between 1k and 100k samples: 1k = 1000^2 = 1.000.

Is SVM fast?

For all test cases, the patched scikit-learn SVM is at least 65 times faster than stock implementation.

Which SVM kernel is fastest?

linear SVM
It seems like there is a significant difference between the two types, for ‘fit’ (Table 1), notably linear SVM is twice as fast as its kernel counterpart, PCA is ~30 times faster, and K-means is around 40 times faster.

Which SVM kernel is the fastest?

Can SVM use GPU?

scikit-svm will never support GPU.

Can SVM be parallel?

To speed up the process of training SVM, parallel methods have been proposed by splitting the problem into smaller subsets and training a network to assign samples of different subsets. …

Why are SVM algorithms so slow?

One of the primary reasons popular libraries SVM algorithms are slow is because they are not incremental. They require the entire dataset to be in RAM all at once. So if you have a million data points, it’s going to run kind of slow.

Why is my SVM not working properly?

The most likely explanation is that you’re using too many training examples for your SVM implementation. SVMs are based around a kernel function. Most implementations explicitly store this as an NxN matrix of distances between the training points to avoid computing entries over and over again.

What is the difference between linear and kernelized SVM?

One situation where this comes up is, with a linear SVM you can optimize on the coefficients on the dimensions directly, whereas with a kernelized SVM you have to optimize a coefficient for each point. With a lot more points than dimensions, the solution space is smaller for the linear SVM.

How can I improve the performance of my SVM?

A much better way to deal with this is to just not use all of the data, since most of it will be redundant from the SVM’s perspective (it only benefits from having more data near the decision boundaries). A good starting place would be to randomly discard 90\% of the training data, and see what performance looks like.