File:Learning Curves (Naive Bayes).png

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Summary

Description
English: A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, we will not benefit much from more training data. In the following plot you can see an example: naive Bayes roughly converges to a low score.
Date
Source https://scikit-learn.org/stable/modules/learning_curve.html
Author scikit-learn developers

Licensing

Copyright © The author

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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This software is provided by The author and contributors "as is" and any express or implied warranties, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall The author and contributors be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.

Captions

Learning curve showing training score and cross validation score

Items portrayed in this file

depicts

20 July 2015

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Date/TimeThumbnailDimensionsUserComment
current09:01, 15 February 2019Thumbnail for version as of 09:01, 15 February 2019640 × 480 (41 KB)Justin OrmontUser created page with UploadWizard
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