5.12.5. Support Vector Machine (clip0243 action)

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5.12.5. Support Vector Machine (clip0243 action)

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Function: R_SVMe1071

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Short description:


Create a SVM model.


Long Description:


Create a predictive model based on “Support Vector Machine” (SVM).


SVM models are interesting from a purely theoretical point-of-view because they have mathematical properties that allows researcher to write many scholary articles about them.


The SVM algorithm is included in Anatella mainly because of historical reasons (and for explanatory/teaching purposes). Indeed, from a pratical point-of-view, SVM are not very useful anymore because:

The SVM algorithm do not scale well: it crashes (typically because of RAM memory issues) on most common-size datasets

The SVM algorithm is extremely Slow (several orders of magnitude slower than any other algorithm)

Compared to other modeling algorithms, the SVM algorithm is not very accurate (other algorithms have typically higher AUC, and higher accuracy). This fact is visible in all datamining competitions (KDD cups, Kaggle) where SVM always ranks amongst the worst algorithms.