sklearn.svm Module: Difference between revisions

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** <code>[[sklearn.ensemble]]</code>, a collection of [[Decision Tree Ensemble Learning System]]s.
** <code>[[sklearn.ensemble]]</code>, a collection of [[Decision Tree Ensemble Learning System]]s.
** <code>[[sklearn.metrics]]</code>, a collection of [[Metric]]s [[Subroutine]]s.
** <code>[[sklearn.metrics]]</code>, a collection of [[Metric]]s [[Subroutine]]s.
** <code>[[sklearn.covariance]]</code>,a collection of [[Covariance Estimator]]s.
** <code>[[sklearn.covariance]]</code>,a collection of [[Covariance Estimator]]s.
** <code>[[sklearn.cluster.bicluster]]</code>, a collection of [[Spectral Biclustering Algorithm]]s.
** <code>[[sklearn.cluster.bicluster]]</code>, a collection of [[Spectral Biclustering Algorithm]]s.
** <code>[[sklearn.linear_model]]</code>, a collection of [[Linear Model Regression System]]s.
** <code>[[sklearn.linear_model]]</code>, a collection of [[Linear Model Regression System]]s.

Latest revision as of 17:08, 1 June 2024

A 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.