sklearn.svm Module

A sklearn.svm Module is an sklearn module of Support Vector Machine Systems.

• AKA: Scikit-Learn Support Vector Machines Class.
• Context:
  • It require to call/select a Support Vector Machine System :
    • sklearn.svm.SVM_ModelName(self, arguments) or simply sklearn.svm.SVM_ModelName()

      where SVM_ModelName is the name of the selected support vector machine system.

• Example(s)
  • sklearn.svm.LinearSVC, a Linear Support Vector Classification System;
  • sklearn.svm.LinearSVR, a Linear Support Vector Regression System;
  • sklearn.svm.NuSVC, a Nu-Support Vector Classification System;
  • sklearn.svm.NuSVR, a Nu Support Vector Regression System;
  • sklearn.svm.OneClassSVM, an Unsupervised Outlier Detection System;
  • sklearn.svm.SVC, a C-Support Vector Classification System;
  • sklearn.svm.SVR, an Epsilon-Support Vector Regression System.
  • …
• Counter-Example(s):
  • sklearn.tree, a collection of Decision-tree Learning systems.
  • sklearn.manifold, a collection of Manifold Learning Systems.
  • sklearn.ensemble, a collection of Decision Tree Ensemble Learning Systems.
  • sklearn.metrics, a collection of Metrics Subroutines.
  • sklearn.covariance,a collection of Covariance Estimators.
  • sklearn.cluster.bicluster, a collection of Spectral Biclustering Algorithms.
  • sklearn.linear_model, a collection of Linear Model Regression Systems.
  • sklearn.neighbors, a collection of K Nearest Neighbors Algorithms.
  • sklearn.neural_network, a collection of Neural Network Systems.
• See: Restricted Boltzmann Machines, Artificial Neural Network, Classification System, Regression System, Unsupervised Learning System, Supervised Learning System.

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References

2018

• (Scikit-learn, 2018) ⇒ http://scikit-learn.org/stable/modules/svm.html#svm Retrieved: 2018-03-11
  • QUOTE: Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

    The advantages of support vector machines are:

    • Effective in high dimensional spaces.
    • Still effective in cases where number of dimensions is greater than the number of samples.
    • Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
    • Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of support vector machines include:

• If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.
• SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation (see Scores and probabilities, below).

The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64.

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