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Frequently Asked Questions

What is a one class SVM?

One-class SVMs are a special case of support vector machine. First, data is modelled and the algorithm is trained. Then when new data are encountered their position relative to the “normal” data (or inliers) from training can be used to determine whether it is “out of class” or not — in other words, whether it is unusual or not.

What is SVM in scikit-learn?

SVM in Scikit-learn supports both sparse and dense sample vectors as input. Scikit-learn provides three classes namely SVC, NuSVC and LinearSVC which can perform multiclass-class classification. It is C-support vector classification whose implementation is based on libsvm. The module used by scikit-learn is sklearn.svm.SVC.

What is a one-class SVM for novelty detection?

Click here to download the full example code or to run this example in your browser via Binder An example using a one-class SVM for novelty detection. One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set.

What is the difference between SVC and scikit-learn?

Scikit-learn provides three classes namely SVR, NuSVR and LinearSVR as three different implementations of SVR. It is Epsilon-support vector regression whose implementation is based on libsvm. As opposite to SVC There are two free parameters in the model namely ‘C’ and ‘epsilon’.


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