Experiences with big data by Srinivasan Seshadri
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The document discusses key principles and axioms surrounding the use of big data, emphasizing the importance of measurement, the distinction between correlation and causation, and the value of feature engineering. It explores the interplay between human intuition and machine computation in data analysis and highlights the significance of outliers and ensembles in modeling. The author encourages a focused approach to data science, advocating for clear metrics and the iterative improvement of solutions.
Experiences with big data by Srinivasan Seshadri
- 4. AXIOMS • Measure, Measure, Measure • Garbage in, Garbage out • Correlation is not Causation • More Data Beats Cleverer Algorithms • Algorithms that do better with more data are more interesting • Independent Sources Of data add new signals • Feature Engineering is the key to being a good data scientist • How do machines and Human interplay in Big Data? • Learn many models ‐ ensembles • Outliers are always interesting..
- 5. MEASURE, MEASURE, MEASURE • Have a Hypothesis • Create a metric to determine if hypothesis is correct • Build a solution that can be measured • Iterate If you can not measure it you can not improve it – Lord Kelvin
- 8. CORRELATIONS
- 9. CORRELATION IS NOT CAUSATION • Correlation in Data Need Not Imply Correlation in Real Life • Can find random correlations in large amounts of data • Correlation Does Not Imply Causation
- 12. MORE DATA BEATS CLEVERER ALGORITHMS • Adding IMDB data For Netflix prize • Adding Protein Expression Data or Patient Data to Gene Expression Data • Bag of Words Approach for Word Sense Disambiguation
- 13. WORD SENSE DISAMBIGUATION • Bank • Sloping Land Alongside a river or a lake. It typically has thick vegetation growing.. • A financial institution that takes deposits from some customers and gives loans to others who require the money. To disambiguate in typical sentences look for co‐occurrences of words with words in definition. Unsupervised Learning. Bootstrap a model. The pilot landed the plane on the Hudson River amongst several boats and an appreciative audience cheered from the banks of the river. He issued a check and took it to the bank so he could transfer money. Can look for frequent co‐occurrences with each sense of the word (boats and check respectively) and build a larger bag of words in which to disambiguate.
- 16. FEATURE ENGINEERING CITYY 1 LAT. CITY 1 LNG. CITY 2 LAT. CITY 2 LNG. DRIVABLE? 123.24 46.71 121.33 47.34 Yes 123.24 56.91 121.33 55.23 Yes 123.24 46.71 121.33 55.34 No 123.24 46.71 130.99 47.34 No
- 17. FEATURE ENGINEERING DISTANCE (MI.) DRIVABLE? 14 Yes 28 Yes 705 No 2432 No
- 18. OF HUMANS AND MACHINES • Partnership is important • Aha moment and the strategy comes from humans.. • Machines do the hard work of calculating fast and do not tire • Maybe some day Machines will be able to do more than they are asked to do explicitly.. Today Explicit Instructions are the norm..
- 19. ENSEMBLES ‐ OUTLIERS ARE NOT INTERESTING – FOR CLASSIFIERS • Learn many models from random subsets of training data • Effect of outliers is reduced on a majority of the models • Random Forests
- 20. OUTLIERS ARE ALWAYS INTERESTING FOR RANKING PROBLEMS • You have to be so good that they can not ignore you • My personal thesis: Average in everything is boring. Be outstanding in something. • Outliers along some dimension always have interesting information – whenever you are combining multiple variables to come up with one global rank • Search • Job Interviews!