# Difference between revisions of "sklearn.svm Module"

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* <B>Context:</B> | * <B>Context:</B> | ||

** It require to call/select a [[Support Vector Machine System]] : | ** It require to call/select a [[Support Vector Machine System]] : | ||

− | *** <code>[[sklearn.svm]].<span style="font-weight:italic; color:Green">SVM_ModelName(self, arguments)</i></code> or simply <code>[[sklearn.svm]].<span style="font-weight:italic; color:Green">SVM_ModelName()</i></code> <P>where <i>SVM_ModelName</i> is the name of the selected [[support vector machine system]]. | + | *** <code>[[sklearn.svm Module|sklearn.svm]].<span style="font-weight:italic; color:Green">SVM_ModelName(self, arguments)</i></code> or simply <code>[[sklearn.svm Module|sklearn.svm]].<span style="font-weight:italic; color:Green">SVM_ModelName()</i></code> <P>where <i>SVM_ModelName</i> is the name of the selected [[support vector machine system]]. |

* <B>Example(s)</B> | * <B>Example(s)</B> | ||

** <code>[[sklearn.svm.LinearSVC]]</code>, a [[Linear Support Vector Classification System]]; | ** <code>[[sklearn.svm.LinearSVC]]</code>, a [[Linear Support Vector Classification System]]; |

## Latest revision as of 20:45, 23 December 2019

An 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.

- It require to call/select a 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.

## 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.

- QUOTE: Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

- 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`

.

- The disadvantages of support vector machines include: