1. scikit-learn
Machine Learning in Python
Data Tuesday - Feb. 26 2013 - Paris
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2. • Library of Machine Learning models
• Simple fit / predict / transform API
• Python / NumPy / SciPy / Cython
& wrappers for libsvm / liblinear
• Model Assessment, Selection & Ensembles
• Some support for multi-core
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3. Possible Applications
• Text Classification / Sequence Tagging NLP
• Computer Vision / Robotics
• Learning To Rank - IR and advertisement
• Statistical Analysis of the Brain: fMRI / MEG
• Astronomy, Biology, Social Sciences...
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7. Example:
Training a Model for
Face Recognition
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8. Total dataset size:
n_samples: 1288, n_features: 1850, n_classes: 7
Extracting the top 150 eigenfaces from 966 faces
done in 0.466s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.056s
Fitting the SVM classifier to the training set
done in 18.549s
Predicting people's names on the test set
done in 0.062s
precision recall f1-score support
Ariel Sharon 0.90 0.75 0.82 12
Colin Powell 0.78 0.94 0.85 62
Donald Rumsfeld 0.86 0.72 0.78 25
George W Bush 0.89 0.96 0.92 141
Gerhard Schroeder 0.92 0.74 0.82 31
Hugo Chavez 0.90 0.53 0.67 17
Tony Blair 0.81 0.74 0.77 34
avg / total 0.86 0.86 0.86 322
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11. Contributors
• GitHub-centric contribution workflow
• each pull request needs 2 x [+1] reviews
• code + tests + doc + example
• 92% test coverage / Continuous Integr.
• 4 major releases per years + 4 bugfix rel.
• 66 contributors for release 0.13
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12. Users
• We support users on & ML
• 200+ questions tagged with [scikit-learn]
• Many competitors + benchmarks
• 500+ answers on ongoing user survey
• 60% academics / 40% from industry
• Some data-drive Startups use sklearn
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13. Thank you!
• http://scikit-learn.org - Main Project + doc
• @ogrisel on twitter
• http://ogrisel.com - ML Consultancy (soon)
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15. Caveat Emptor
• Domain specific tooling kept to a minimum
• Some feature extraction for Bag of
Words Text Analysis
• Some functions for extracting image
patches
• Domain integration is the responsibility of
the user or 3rd party libraries
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