Big Data and Predictive Modeling

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Big Data and Predictive Modeling Olivier Grisel — @ogrisel Web We Can March 21, 2015

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About me

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Big Data as a buzzword

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Triumph of the Nerds: Nate Silver Wins in 50 States http://mashable.com/2012/11/07/nate-silver-wins/

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Triumph of the Nerds: Nate Silver Wins in 50 States http://mashable.com/2012/11/07/nate-silver-wins/

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Nate Silver’s election model, Big Data? $ git clone gh:jseabold/538model $ du -h 538model/data 188K 538model/data

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15% of the capacity of a 3’5 ﬂoppy disk

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Regionator 3000 http://labs.data-publica.com/regionator3000/

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http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/

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http://transports.blog.lemonde.fr/2014/06/05/regionator-la- carte-de-france-dessinee-par-les-trajets-quotidiens/

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http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10

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http://www.insee.fr/fr/themes/detail.asp? reg_id=99&ref_id=mobilite-professionnelle-10

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120% of the capacity of a 3’5 ﬂoppy disk

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Big Data ≠ Predictive Analytics

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Predictive Analytics ≠ Descriptive Analytics

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Goals of this talk • What Big Data actually is or isn’t • Introduce predictive modeling concepts • Contrast predictive analytics vs descriptive analytics

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How big is Big Data?

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– Wikipedia “Big data is a blanket term for any collection of data sets so large and complex that it becomes difﬁcult to process using on-hand database management tools or traditional data processing applications.”

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Not Big Data • Data that ﬁts on a spreadsheet • Data that can be analyzed in RAM (< 10 GB) • Data operations that can be performed quickly by a traditional database, e.g. single node PostgreSQL server

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Reading the full content of a 1TB HDD at 100MB/s: 2 hours 45 minutes

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Canonical Big Data problem: indexing the Web • Inverted index on tera bytes of text data • Process each HTML page as a URL + bag of words • For each word, aggregate the list of page URLs • 2 billion HTML pages: 100TB >10 days just to read sequentially

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Other Big Data examples • GSM location event log from telco • Transaction log of a big retail network • Raw trafﬁc data on a large website • Activity records for a service with 10s of millions of users

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Not Big Data • Polls data (~10K data points) • Census data (~10M data points) • Real estate transactions data (~10M data points) • Any dataset publicly available for download

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What is Predictive Analytics? and Machine Learning

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Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes Sold 300k 1.5M 2.2M

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Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target

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Type # rooms Surface m2 Floor Public Transports Apartment 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples Sold 300k 1.5M 2.2M target Apartment 2 35 3 Yes ?

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Predictive Modeling • Automated predictions of outcome on new data • Alternative to hard-coded rules written by experts • Extract the structure of historical data • Statistical tools to summarize the training data into an executable predictive model

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Training text docs images sounds transactions Labels Machine Learning Algorithm Model Predictive Modeling Data Flow Feature vectors

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New text doc image sound transaction Model Expected Label Predictive Modeling Data Flow Feature vector Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors

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Descriptive vs Predictive

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Descriptive Statistics • Ex: Sales by (day, months, year) x region • Graphical visualization: get insights, tell a story to explain what’s happening in the data • Realm of Business Intelligence: reports & dashboard for managers • A wrong decision can be very costly • Small number of important decisions made by a human

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Predictive Statistics • Ex: Movie recommendations or targeted ads • Embedded in a user service to make it more useful / attractive / proﬁtable • A wrong individual decision is not costly • Large number of small automated decisions • Humans would not be fast enough to make the predictions

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Mixed models • Predictive modeling to identify interesting subsets of the data • Ex: Fraud detection, churn forecasting • Help human decision makers focus on important cases • Human expert feedback to improve predictive models

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Key takeaway points:

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• Big Data ≠ Predictive Analytics • Predictive Analytics • Automated decision making embedded in products (e.g. recommenders) • Individual bad decisions are typically not costly • Descriptive Analytics • Business Intelligence: human decision making • Individual bad decisions can be very costly

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Thank you! Questions? @Inria @ogrisel http://scikit-learn.org

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Bonus tracks

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Back to the Regionator What if we did not have census data on daily mobility?

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Back to the Regionator • Use raw daily telco logs • Group By (phone, day) to extract daily trips • Join By GPS coordinates to “departement” names • Filter out small trips • Group By (home, work) “departements” • Count

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Tools for predictive analytics

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SPSS MATLAB

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SPSS MATLAB

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New text doc image sound transaction Model Expected Label Small data Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

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New text doc image sound transaction Model Expected Label Small / Medium data Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

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New text doc image sound transaction Model Expected Label Small / Medium data with Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

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New text doc image sound transaction Model Expected Label Small / Medium data with Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors Feature vector

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Predictive Analytics on Big Data

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Model Expected Label Big data with Machine Learning Algorithm New text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector

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Model Expected Label Big data with Machine Learning Algorithm New text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector

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Model Expected Label Big data with Machine Learning Algorithm New text doc image sound transaction Training text docs images sounds transactions Labels Feature vectors Feature vector

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BIG DATA

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BIG DATA small(er) data

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BIG DATA small(er) data

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From Big to Small • Feature extraction often shrinks data • Filter / Join / Group By / Count • Machine Learning performed on aggregates • Sampling for fast in-memory iterative modeling

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Data size and modeling quality

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– Peter Norvig, Research Director, Google “We don’t have better algorithms. We just have more data.”

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More data beats better models?

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http://technocalifornia.blogspot.fr/2012/07/more-data-or-better- models.html

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Let’s train a parametric model to read handwritten digits from gray level pixels.

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model stops improving

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Bias vs Variance

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high bias

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high bias high variance

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high bias high variance low variance

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Variance solution #1: collect more samples

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Let’s train a non-parametric model to read handwritten digits from gray level pixels.

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high variance almost no bias variance decreasing with #samples

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Bias solution #1: non-parametric models

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Type # rooms Surface (m2) Floor Public Transp. Apart. 3 65 2 Yes House 5 110 NA No Duplex 4 95 4 Yes features samples

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Type # rooms Surface (m2) Floor Public Transp. School (km) Flood plain Apart. 3 65 2 Yes 1.0 No House 5 110 NA No 25.0 Yes Duplex 4 95 4 Yes 0.5 No features samples

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Bias solution #2: richer features

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Data has 2 dimensions: # samples and # features

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• Parametric e.g. linear model (traditional stats) vs Non-parametric e.g. Random Forests, Neural Networks (Machine Learning) • Understand a model with 10% accuracy vs blindly trust a model with 90% accuracy • Simple models e.g. F = m a, F = - G (m1 + m2) / r^2 will not become false(r) because of big data • New problems can be tackled: computer vision, speech recognition, natural language understanding

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• the (experimental) scientiﬁc method introduced by Karl Popper is based on the falsiﬁability of formulated hypotheses • theory is correct as long as past predictions hold in new experiments • machine learning train-validation-test splits and cross-validation is similar in spirit • ml model is just a complex theory: correct as long as its predictions still hold