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scikit-learn                            Machine Learning in Python                         Data Tuesday - Feb. 26 2013 - P...
• Library of Machine Learning models                     • Simple fit / predict / transform API                     • Pyth...
Possible Applications                     • Text Classification / Sequence Tagging NLP                     • Computer Visio...
dimanche 24 février 13
dimanche 24 février 13
dimanche 24 février 13
Example:                         Training a Model for                          Face Recognitiondimanche 24 février 13
Total dataset size:   n_samples: 1288, n_features: 1850, n_classes: 7   Extracting the top 150 eigenfaces from 966 faces  ...
dimanche 24 février 13
Learned Eigen Facesdimanche 24 février 13
Contributors                     • GitHub-centric contribution workflow                      • each pull request needs 2 x ...
Users                     • We support users on                  & ML                     • 200+ questions tagged with [sc...
Thank you!                     • http://scikit-learn.org - Main Project + doc                     • @ogrisel on twitter   ...
Backup Slidesdimanche 24 février 13
Caveat Emptor                     • Domain specific tooling kept to a minimum                      • Some feature extractio...
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6 scikit-learn - Data Tuesday 26 fev 2013

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Transcript of "6 scikit-learn - Data Tuesday 26 fev 2013"

  1. 1. scikit-learn Machine Learning in Python Data Tuesday - Feb. 26 2013 - Parisdimanche 24 février 13
  2. 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-coredimanche 24 février 13
  3. 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...dimanche 24 février 13
  4. 4. dimanche 24 février 13
  5. 5. dimanche 24 février 13
  6. 6. dimanche 24 février 13
  7. 7. Example: Training a Model for Face Recognitiondimanche 24 février 13
  8. 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 peoples 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 322dimanche 24 février 13
  9. 9. dimanche 24 février 13
  10. 10. Learned Eigen Facesdimanche 24 février 13
  11. 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 0.13dimanche 24 février 13
  12. 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 sklearndimanche 24 février 13
  13. 13. Thank you! • http://scikit-learn.org - Main Project + doc • @ogrisel on twitter • http://ogrisel.com - ML Consultancy (soon)dimanche 24 février 13
  14. 14. Backup Slidesdimanche 24 février 13
  15. 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 librariesdimanche 24 février 13
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