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FIX: broken link in SVM doc
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doc/modules/svm.rst

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@@ -304,12 +304,10 @@ Tips on Practical Use
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* Support Vector Machine algorithms are not scale invariant, so **it
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is highly recommended to scale your data**. For example, scale each
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attribute on the input vector X to [0,1] or [-1,+1], or standardize
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it to have mean 0 and variance 1. Note that the *same* scaling
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must be applied to the test vector to obtain meaningful
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results. See `The CookBook
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<https://sourceforge.net/apps/trac/scikit-learn/wiki/CookBook>`_
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for some examples on scaling.
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attribute on the input vector X to [0,1] or [-1,+1], or standardize it
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to have mean 0 and variance 1. Note that the *same* scaling must be
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applied to the test vector to obtain meaningful results. See section
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:ref:`preprocessing` for more details on scaling and normalization.
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* Parameter nu in NuSVC/OneClassSVM/NuSVR approximates the fraction
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of training errors and support vectors.

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