Update description of OneClassSVM for outlier detection#17710
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rubywerman wants to merge 1 commit intoscikit-learn:masterfrom
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Update description of OneClassSVM for outlier detection#17710rubywerman wants to merge 1 commit intoscikit-learn:masterfrom
rubywerman wants to merge 1 commit intoscikit-learn:masterfrom
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glemaitre
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Jun 25, 2020
| The :class:`svm.OneClassSVM` is known to be sensitive to outliers and thus | ||
| does not perform very well for outlier detection. Finally, | ||
| :class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns | ||
| does not perform very well for outlier detection. That being said, outlier detection in high-dimension, or without any assumptions on the distribution of the inlying data is very challenging. Thus, a :class:`svm.OneClassSVM` might give useful results in these situations depending on the value of its hyperparameters. Finally, :class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns |
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Could you format your comment such that it fits 79 characters
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yes - just linked a new PR fixing the comments you raised
| The :class:`svm.OneClassSVM` is known to be sensitive to outliers and thus | ||
| does not perform very well for outlier detection. Finally, | ||
| :class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns | ||
| does not perform very well for outlier detection. That being said, outlier detection in high-dimension, or without any assumptions on the distribution of the inlying data is very challenging. Thus, a :class:`svm.OneClassSVM` might give useful results in these situations depending on the value of its hyperparameters. Finally, :class:`covariance.EllipticEnvelope` assumes the data is Gaussian and learns |
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Instead of using might give results ... I would rather mention something along the line.
OneClassSVM might still be used with outlier detection but requires fine-tuning of its hyperparameter nu to handle outliers and not overfit those.
| :target: ../auto_examples/neighbors/sphx_glr_plot_lof_novelty_detection.html | ||
| :align: center | ||
| :scale: 75% | ||
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You can avoid this change since this is not linked
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Reference Issues/PRs
Fixes part of #3466
What does this implement/fix? Explain your changes.
This PR clarifies that One Class SVM can be used for outlier detection and novelty detection. Originally, the doc incorrectly suggested One Class SVM should only be used for novelty detection. I utilized the explanation on One Class SVM from here, which was already linked as an example.
Any other comments?