The documentation is sklearn.svm.SVC. And when I choose this model, I'm mindful of the dataset size. Extracted: The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using instead.
The SVM module (SVC, NuSVC, etc) is a wrapper around the libsvm library and supports different kernels while LinearSVC is based on liblinear and only supports a linear kernel.
SVC(kernel="linear") is better LinearSVC is better Doesn't matter Can someone explain when to use LinearSVC vs. SVC(kernel="linear")? It seems like LinearSVC is marginally better than SVC and is usually more finicky. But if scikit decided to spend time on implementing a specific case for linear classification, why wouldn't LinearSVC outperform SVC?
A .svc file contains a WCF-specific processing directive (@ServiceHost) that allows the WCF hosting infrastructure to activate hosted services in response to incoming messages. Can someone please guide me as to how I can create this file so that I may host it? Thanks!
c# - How do I generate the .svc file? - Stack Overflow
In my experience, an SVC file is a WCF service - create a WCF application from the projects list and then do an "Add new item" and add a new WCF service. For it to do anything it will need the appropriate bindings and endpoints configuring and wiring up - before it can be called.
How do I add a .svc file in Visual Studio - Stack Overflow
SVM-training with nonlinear-kernels, which is default in sklearn's SVC, is complexity-wise approximately: O(n_samples^2 * n_features) link to some question with this approximation given by one of sklearn's devs. This applies to the SMO-algorithm used within libsvm, which is the core-solver in sklearn for this type of problem.