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Machine Learning Documentation Initiative

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. What is Machine Learning (ML).

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Machine Learning Documentation Initiative

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  1. Machine LearningDocumentation 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. Coding Editing Linkage Collection Statistics Canada • Statistique Canada

  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 Statistics Canada • Statistique Canada

  4. Classes of ML SUPERVISED 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 Statistics Canada • Statistique Canada

  5. Classes of ML 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 Statistics Canada • Statistique Canada

  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 Statistics Canada • Statistique Canada

  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 Statistics Canada • Statistique Canada

  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 Statistics Canada • Statistique Canada

  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 Statistics Canada • Statistique Canada

  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? Statistics Canada • Statistique Canada

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

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