Benchmarking Machine Learning Tools - H2O World Conference - Nov 2015

Transcript

1. Benchmarking Machine Learning Tools for Scalability, Speed and Accuracy Szilárd Pafka, PhD Chief Scientist, Epoch H2O World Conference, Mountain View Nov 2015
3. Disclaimer: I am not representing my employer (Epoch) in this talk I cannot confirm nor deny if Epoch is using or not any of the methods, tools, results etc. mentioned in this talk. The results presented in this talk should not be considered as any indication whether Epoch is using these methods, tools, results etc. or not.
5. I usually use other people’s code [...] it is usually not “efficient” (from time budget perspective) to write my own algorithm [...] I can find open source code for what I want to do, and my time is much better spent doing research and feature engineering -- Owen Zhang http://blog.kaggle.com/2015/06/22/profiling-top-kagglers-owen-zhang-currently-1-in-the-world/
6. Data Size for Supervised Learning # records: <10M 10M-10B >10B
7. Data Size for Non-Linear Supervised Learning # records: <1M 1M-100M >100M
8. binary classification, 10M records numeric & categorical features, non-sparse
11. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf
12. http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf http://lowrank.net/nikos/pubs/empirical.pdf
15. - R packages - Python scikit-learn - Vowpal Wabbit - H2O - xgboost - Spark MLlib
16. - R packages 30% - Python scikit-learn 40% - Vowpal Wabbit 8% - H2O 10% - xgboost 8% - Spark MLlib 6%
17. - R packages 30% - Python scikit-learn 40% - Vowpal Wabbit 8% - H2O 10% - xgboost 8% - Spark MLlib 6% - a few others
18. - R packages 30% - Python scikit-learn 40% - Vowpal Wabbit 8% - H2O 10% - xgboost 8% - Spark MLlib 6% - a few others
19. EC2
20. Distributed computation generally is hard, because it adds an additional layer of complexity and [network] communication overhead. The ideal case is scaling linearly with the number of nodes; that’s rarely the case. Emerging evidence shows that very often, one big machine, or even a laptop, outperforms a cluster. http://fastml.com/the-emperors-new-clothes-distributed-machine-learning/
22. n = 10K, 100K, 1M, 10M, 100M Training time RAM usage AUC CPU % by core read data, pre-process, score test data
27. linear tops off (data size) (accuracy)
28. linear tops off more data & better algo (data size) (accuracy)
29. linear tops off more data & better algo random forest on 1% of data beats linear on all data (data size) (accuracy)
31. 10x
36. http://datascience.la/benchmarking-random-forest-implementations/#comment-53599
44. we will continue to run large [...] jobs to scan petabytes of [...] data to extract interesting features, but this paper explores the interesting possibility of switching over to a multi-core, shared-memory system for efficient execution on more refined datasets [...] e.g., machine learning http://openproceedings.org/2014/conf/edbt/KumarGDL14.pdf
47. learn_rate = 0.1, max_depth = 6, n_trees = 300 learn_rate = 0.01, max_depth = 16, n_trees = 1000
54. Non-Linear Supervised Learning
55. # records: <1M 1M-100M >100M Non-Linear Supervised Learning