Upgrade to Pro — share decks privately, control downloads, hide ads and more …

A Benchmark of Open Source Tools for Machine Le...

szilard
July 02, 2017
1
320

A Benchmark of Open Source Tools for Machine Learning from R - UseR! 2017 Conference - Brussels, July, 2007

szilard

July 02, 2017
Tweet

More Decks by szilard

See All by szilard
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Data Con LA - Oct 2020
szilard
0
140
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - Albuquerque Machine Learning Meetup (Online) - Aug 2020
szilard
0
94
Better than Deep Learning: Gradient Boosting Machines (GBM) - eRum conference - invited talk - June 2020
szilard
0
92
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - LA Data Science Meetup - February 2020
szilard
0
83
A Random Walk in Data Science and Machine Learning in Practice - CEU, Business Analytics Masters - Budapest, Febr 2020
szilard
0
290
Better than My Meetup/Conference Talks: Going Deeper in Various GBM Topics - GBM Advanced Workshop - Budapest, Nov 2019
szilard
0
54
Gradient Boosting Machines (GBM): From Zero to Hero (with R and Python Code) - Budapest BI Forum, Budapest, Nov 2019
szilard
0
130
Make Machine Learning Boring Again: Best Practices for Using Machine Learning in Businesses - LA Data Science Meetup - Playa Vista, August 2019
szilard
0
100
Better than Deep Learning: Gradient Boosting Machines (GBM) / 2019 edition - Budapest R and Data Science Meetups - Budapest, June 2019
szilard
0
79

Featured

See All Featured
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
47
2.1k
Build your cross-platform service in a week with App Engine
jlugia
229
18k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
26
1.4k
Large-scale JavaScript Application Architecture
addyosmani
510
110k
Facilitating Awesome Meetings
lara
50
6.1k
It's Worth the Effort
3n
183
27k
Testing 201, or: Great Expectations
jmmastey
38
7.1k
Templates, Plugins, & Blocks: Oh My! Creating the theme that thinks of everything
marktimemedia
26
2.1k
Writing Fast Ruby
sferik
627
61k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
10
720
GraphQLの誤解/rethinking-graphql
sonatard
67
10k
What’s in a name? Adding method to the madness
productmarketing
PRO
22
3.1k

Transcript

  1. A Benchmark of Open Source Tools for Machine Learning from

    R Szilárd Pafka, PhD Chief Scientist, Epoch useR! 2017 Conference Brussels, July 2017
  2. None

  3. Disclaimer: I am not representing my employer (Epoch) in this

    talk I cannot confirm nor deny if Epoch is using any of the methods, tools, results etc. mentioned in this talk
  4. None
  5. None
  6. None
  7. None
  8. None
  9. None
  10. None

  11. binary classification, 10M records numeric & categorical features, non-sparse

  12. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf

  13. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf

  14. None
  15. None
  16. None
  17. None

  18. EC2

  19. n = 10K, 100K, 1M, 10M, 100M Training time RAM

    usage AUC CPU % by core read data, pre-process, score test data

  20. n = 10K, 100K, 1M, 10M, 100M Training time RAM

    usage AUC CPU % by core read data, pre-process, score test data
  21. None
  22. None
  23. None
  24. None
  25. None
  26. None
  27. None

  28. 10x

  29. None
  30. None
  31. None
  32. None
  33. None

  34. http://datascience.la/benchmarking-random-forest-implementations/#comment-53599

  35. None
  36. None
  37. None
  38. None
  39. None
  40. None

  41. Best linear: 71.1

  42. None
  43. None

  44. learn_rate = 0.1, max_depth = 6, n_trees = 300 learn_rate

    = 0.01, max_depth = 16, n_trees = 1000
  45. None
  46. None
  47. None
  48. None
  49. None
  50. None
  51. None

  52. ...

  53. None
  54. None
  55. None
  56. None
  57. None
  58. None
  59. None
  60. None
  61. None
  62. None
  63. None

  64. R++

  65. None
  66. None
  67. None
  68. None
  69. None
  70. None
  71. None
  72. None
  73. None
  74. None
  75. None
  76. None