8000 [MRG+1] Add note about the size of a random forest model #6276 by Morikko · Pull Request #8437 · scikit-learn/scikit-learn · GitHub
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Merged
merged 3 commits into from
Feb 23, 2017
Merged

[MRG+1] Add note about the size of a random forest model #6276 #8437

merged 3 commits into from
Feb 23, 2017

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Morikko
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@Morikko Morikko commented Feb 22, 2017

Reference Issue

Fixes #6276

What does this implement/fix? Explain your changes.

Add a note in the section random forest for informing how to reduce the size of the resulting model.

Any other comments?

First contribution, remarks are welcome.

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codecov bot commented Feb 22, 2017

Codecov Report

Merging #8437 into master will increase coverage by <.01%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #8437      +/-   ##
==========================================
+ Coverage   95.47%   95.47%   +<.01%     
==========================================
  Files         342      342              
  Lines       60902    60907       +5     
==========================================
+ Hits        58149    58154       +5     
  Misses       2753     2753
Impacted Files Coverage Δ
sklearn/cluster/dbscan_.py 100% <ø> (ø)
sklearn/cluster/tests/test_dbscan.py 100% <ø> (ø)

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@jnothman jnothman left a comment

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Otherwise LGTM


The size of the model with the default parameters is :math:`O( M * N * log (N) )`,
where :math:`M` is the number of trees and :math:`N` is the number of samples.
In order to reduce the size of the model, you can adapt those parameters:
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I don't think adapt is the right word. You can say "change" or "modify" or "tune"

@jnothman jnothman changed the title [MRG] Add note about the size of a random forest model #6276 [MRG+1] Add note about the size of a random forest model #6276 Feb 23, 2017
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Thanks!

@jnothman jnothman merged commit 7f084b0 into scikit-learn:master Feb 23, 2017
@Przemo10 Przemo10 mentioned this pull request Mar 17, 2017
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Need a condensed representation for storing random forest classifiers
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