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Support vector machines (SVMs) (version
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What it does This module implements the Support Vector Machine (SVM) classification algorithms. 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:

1- Effective in high dimensional spaces.

2- Still effective in cases where number of dimensions is greater than the number of samples.

3- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.

4- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of support vector machines include:

1- If the number of features is much greater than the number of samples, the method is likely to give poor performances.

2- SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation

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