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Machine Learning for Humans

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Machine Learning for Humans

Toronto Data Science Group
February 25, 2015

Myles Harrison
http://www.everydayanalytics.ca

Published in: Data & Analytics
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Machine Learning for Humans

  1. 1. Machine Learning for Humans Toronto Data Science Group February 25, 2014 Myles Harrison www.everydayanalytics.ca @everydayanalyst
  2. 2. (3 things I have noticed...)
  3. 3. ELI5?
  4. 4. (machine learning)
  5. 5. 1 1 0 1 0 1 0 … b = b = …?
  6. 6. Supervised Learning ELI5 CAT NOT CAT CAT ?
  7. 7. Unsupervised Learning ELI5
  8. 8. (model evaluation)
  9. 9. ACTUAL PREDICTED Confusion Matrix TRUE POSITIVE (TP) FALSE POSITIVE (FP) TRUE NEGATIVE (TN) FALSE NEGATIVE (FN)
  10. 10. CAT NOT CAT
  11. 11. true positive rate (sensitivity) false positive rate (1 – specificity) 1 1 0
  12. 12. (overfitting)
  13. 13. CROSS VALIDATION
  14. 14. Cross-validation K-fold where k=3 1 2 3
  15. 15. Unbalanced Problem
  16. 16. Credit: scikit-learn.org
  17. 17. Machine Learning for Humans • don’t think about techniques, think about types of learning • if you don’t evaluate the performance of your model, then it’s useless • acknowledge the complexities of data, select and tune your model accordingly • make your model easy to train & integrate with!
  18. 18. http://www.everydayanalytics.ca
  19. 19. (fin) ?

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