Machine Learning on Largish Data - Big Data Day LA - June 2015

Transcript

1. Machine Learning on Largish Data Szilárd Pafka, PhD Chief Scientist, Epoch Big Data Day LA 2015 June 2015
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/
21. n = 10K, 100K, 1M, 10M, 100M Training time RAM usage AUC CPU % by core read data, pre-process, score test data
27. vs “More data usually beats better algorithms” http://anand.typepad.com/datawocky/2008/03/more-data-usual.html
28. linear tops off
29. linear tops off more data & better algo
30. linear tops off more data & better algo random forest on 1% of data beats linear on all data
31. linear tops off more data & better algo random forest on 1% of data beats linear on all data
33. 10x
39. I’m of course paranoid that the need for distributed learning is diminishing as individual computing nodes (augmented with GPUs) become increasingly powerful. So I was ready for Jure Leskovec’s workshop talk [at NIPS 2014]. Here is a killer screenshot. -- Paul Mineiro
41. 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
44. learn_rate = 0.1, max_depth = 6, n_trees = 300 learn_rate = 0.01, max_depth = 16, n_trees = 1000
50. Non-Linear Supervised Learning
51. # records: <1M 1M-100M >100M Non-Linear Supervised Learning