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#SVM
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Still effective in cases where number of dimensions is greater than the number of samples.

Tags
#SVM
Question
[default - edit me]
Answer
?

Tags
#SVM
Question
[default - edit me]
Answer
Still effective in cases where number of dimensions is greater than the number of samples.
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1.4. Support Vector Machines — scikit-learn 0.23.2 documentation
(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. <span>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 decisi

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