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]].
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*** <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.



References

2018

The disadvantages of support vector machines include:
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.