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Machine Learning Documentation Initiative Workshop on the Modernisation of Statistical Production Topic iii) Innovation in technology and methods driving.

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Presentation on theme: "Machine Learning Documentation Initiative Workshop on the Modernisation of Statistical Production Topic iii) Innovation in technology and methods driving."— Presentation transcript:

1 Machine Learning Documentation Initiative Workshop on the Modernisation of Statistical Production Topic iii) Innovation in technology and methods driving opportunities for modernisation Kenneth Chu and Claude Poirier Geneva, Switzerland, 15-17 April 2015

2 What is Machine Learning (ML) Application of artificial intelligence in which algorithms use available information to process (or assist the processing of) statistical data 20 applications were reported. 18/11/2015 Statistics Canada Statistique Canada 2 CodingEditingLinkageCollection

3 Why should we consider ML ?  Relatively new discipline of computer science No needs for probabilistic models Less stringent for the BIG Data era  NSOs should all explore the use of ML 18/11/2015 Statistics Canada Statistique Canada 3

4 Classes of ML  Ex.1: Logistic regression [statistics] Training data: Binary response (0:1) and predictors Maximum likelihood leads to model parameters Resulting model is used to predict responses  Ex.2: Support Vector Machines [non-statistics] Training data: Binary response (0:1) and predictors Hyperplanes in the space of predictors separate responses SVM optimisation problem comes from geometry  Decision trees, neural networks, Bayesian networks 18/11/2015 Statistics Canada Statistique Canada 4 SUPERVISED ML

5 Classes of ML 18/11/2015 Statistics Canada Statistique Canada 5 UNSUPERVISED ML  Ex.1: Principal Component Analysis [statistics] PCA summarizes a set of data by finding orthogonal sub-spaces that represent most of the variation There is no longer a response variable in the setting  Ex.2: Cluster Analysis [non-statistics] CA seeks to determine grouping in given data Again, there are no response variables in the setting

6 Applications  Automated Coding Bayesian classifier (Germany): Occupation coding CASCOT (United Kingdom): Occupation coding Indexing utility (Ireland): Individual consumption SVM (New Zealand): Occupation and Qualification 18/11/2015 Statistics Canada Statistique Canada 6

7 Applications  Data Editing Bayesian Networks (Eurostat): Voting intentions Classification Trees (Portugal): Foreign trade data Cluster Analysis (USA): Census of agriculture CART (New Zealand): Census of population Random Forests (New Zealand): Donor imputation Association Analysis (New Zealand): Edit rules 18/11/2015 Statistics Canada Statistique Canada 7

8 Applications  Record Linkage Neither like coding, nor editing Quality of linkages depends on pre-processing more than matching No applications of Machine Learning in official statistics were listed 18/11/2015 Statistics Canada Statistique Canada 8

9 Applications  Other areas – Data collection Classification Tree (USA): Non-response prediction Classification Tree (USA): Reporting errors Naïve Bayes text mining (Italy): Web scraping K-nearest neighbours (Hungary): Tax audit Image Processing (Canada): Remote sensing 18/11/2015 Statistics Canada Statistique Canada 9

10 Concluding remarks  Several machine learning applications  Gap in the area of record linkage  Attention required outside statistical paradigms  Next: Applying Machine Learning on BIG Data Will this be possible only on a case-by-case basis? 18/11/2015 Statistics Canada Statistique Canada 10

11 Thank you Merci  For more information,Pour plus d’information, please contact:veuillez contacter : Claude.Poirier@statcan.gc.ca 18/11/2015 Statistics Canada Statistique Canada 11


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