[MRG+2] modify disadvantage#8521
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jmschrei merged 1 commit intoscikit-learn:masterfrom Mar 4, 2017
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svm can work effectively when feature number is >> number of samples. But to avoid over-fitting usually happens in such situation by choosing appropriate kernel (model selection) is important
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LGTM. |
amueller
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Mar 4, 2017
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| - If the number of features is much greater than the number of | ||
| samples, the method is likely to give poor performances. | ||
| samples, avoid over-fitting in choosing :ref:`svm_kernels` and regularization |
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Looks like its 84 characters, is that a major issue?
Codecov Report
@@ Coverage Diff @@
## master #8521 +/- ##
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Coverage 95.48% 95.48%
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Files 342 342
Lines 60913 60913
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Hits 58160 58160
Misses 2753 2753Continue to review full report at Codecov.
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LGTM as well. |
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Congrats @Ellen-Co2 ! |
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svm can work effectively when feature number is >> number of samples.
But to avoid over-fitting usually happens in such situation by choosing
appropriate kernel (model selection) is important
Reference Issue
<-- Fixes #8450 -->
What does this implement/fix? Explain your changes.
In case of high dimensionality, SVM can still work effectively, but the over-fitting issue still need to be considered, cause the vc dimension might be close to infinite in such case, thus choose of kernel or control the regularization factor "C" is essential.
Any other comments?
To test for over-fitting, use cross validation or larger hold-out can be useful. Check some discussions regarding dimensionality here