Which is the fastest implementation of Python

Fastest SVM implementation that can be used in Python

Firstly, according to the benchmark from scikit-learn (here), scikit-learn is already one of the fastest, if not the fastest SVM package. Therefore, you should consider other ways to speed up your workout.

As suggested by bavaza, you can try multi-threaded training process. If you are using Scikit-learn's GridSearchCV class, you can simply set the n_jobs argument to be larger than the default value of 1 to run the training in parallel at the expense of using more memory. __ You can find the documentation here. An example of using the class is here.

Alternatively, you can take a look at the Shogun Machine Learning Library here

Shogun is designed for large-scale machine learning with wrappers for many popular svm packages and is implemented in C / C ++ with bindings for Python. According to the aforementioned benchmark from Scikit-Learn, the speed is comparable to Scikit-Learn. Other assignments (besides the ones that demonstrated them) may be faster, so it's worth trying.

Finally, you can try to do the dimensional reduction, e.g. Use PCA or randomized PCA to reduce the dimension of your feature vectors. That would speed up the training process. The documentation for the respective classes can be found in these 2 links: PCA, Randomized PCA. For examples of usage, see the Scikit-Learn Examples section.