Committed to Open Source

1 minutes read

scikit-learn is one of my most-used tools, be it at work, or playing in ML competitions. I thought it was high time that I contribute back to this awesome project, and last week one of my pull requests was merged into the master branch!

In sklearn 0.16 (coming soon) you will now be able to automatically weight your samples based on class. Sure you could do this manually before, but now it is also grid-searchable:

X, y = make_classification(n_samples=1000,
                           weights=[0.8, 0.2])
parameters = {'class_weight': [{0: i + 1., 1: 10. - i} for i in range(10)]}
clf = RandomForestClassifier(n_estimators=100)
grid = GridSearchCV(clf, parameters), y)

This dataset is quite messy, and unbalanced, so the weighting scheme for best performance may be a bit unclear. This particular grid-search iterates the sample weights by class from 10:1 to 1:10 and declares the winner:

'class_weight': {0: 7.0, 1: 4.0}

Of course, for the lazy or mega-huge-ensemble wielders, you also have a couple of presets to choose from: 'auto' and 'subsample' which will weight samples inversely proportional to the class frequencies. The 'auto' mode performs this (once) over the entire dataset, while the 'subsample' mode calculates the inverse frequencies of the classes in the bootstrap sample fed to the individual tree estimators (n_estimators times of course).

My pull request had the great, albeit unusual, pleasure of three code reviews. No doubt that Random Forests are one of the go-to classifiers out there, so I don’t blame them for a bit of caution with a new feature! Having my code picked apart by some of the very talented core contributors was a great experience and I learnt a lot from those guys, but the idea that hundreds of thousands of users may one day be running some lines of code that I wrote is a whole other level. It’s an amazing feeling. Now to figure out how to split up time between committing-to and consumption-of the code-base!

If you can’t wait for the 0.16 public release, feel free to grab the development branch code. There’s a ton of other excellent goodies from other contributors in there too.

I truly hope that you can get a bit more out of your ensembles now, let me know if you used it in the comments!

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