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scikit-learn 1.3.2
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# Support Vector Machines

Examples concerning the `sklearn.svm` module.

Non-linear SVM

Non-linear SVM

One-class SVM with non-linear kernel (RBF)

One-class SVM with non-linear kernel (RBF)

Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

Plot the support vectors in LinearSVC

Plot the support vectors in LinearSVC

RBF SVM parameters

RBF SVM parameters

SVM Margins Example

SVM Margins Example

SVM Tie Breaking Example

SVM Tie Breaking Example

SVM with custom kernel

SVM with custom kernel

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes

SVM: Weighted samples

SVM: Weighted samples

Scaling the regularization parameter for SVCs

Scaling the regularization parameter for SVCs

Support Vector Regression (SVR) using linear and non-linear kernels

Support Vector Regression (SVR) using linear and non-linear kernels