8000 [MRG+1] added a new section on model persistence by raulgarreta · Pull Request #3084 · scikit-learn/scikit-learn · GitHub
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[MRG+1] added a new section on model persistence #3084

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raulgarreta
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Fix for issue #1332


In the specific case of the scikit, it may be more interesting to use
joblib's replacement of pickle (``joblib.dump`` & ``joblib.load``),
which is more efficient on big data, but can only pickle to the disk
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...which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators.

@ogrisel
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ogrisel commented Apr 17, 2014

Apart from my comments, this looks good. +1 for merge once they are addressed.

@raulgarreta
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Addressed ogrisel comments in 640bff9

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ogrisel commented Apr 17, 2014

Ping @mblondel @amueller @GaelVaroquaux

@ogrisel ogrisel changed the title [MRG] added a new section on model persistence [MRG+1] added a new section on model persistence Apr 17, 2014
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Coverage Status

Coverage increased (+0.01%) when pulling 640bff9 on raulgarreta:model_persistence_doc into b70a481 on scikit-learn:master.

8000
@@ -22,3 +22,4 @@
Dataset loading utilities <datasets/index.rst>
modules/scaling_strategies.rst
modules/computational_performance.rst
model_persistence.rst
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I'm not sure about this positioning. For example, after (or within) model selection and evaluation seems more coherent.

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+1

@mblondel
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For the sake of consistency, I'd rather avoid the use of "you": we don't address the reader directly in the rest of the doc. Besides that +1 for merge once @jnothman 's comments have been taken into account.

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Fixed by #3317

@jnothman jnothman closed this Jun 27, 2014
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