Machine Learning & Apache Mahout

Machine Learningcon Apache Mahout Domingo Suarez Torres

Machine Learning (ML) Introduction

Definition • Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data (1)1http://en.wikipedia.org/wiki/Machine_learning

• “Machine Learning is programming computers to optimize a performance criterion using example data or past experience” • Intro. To Machine Learning by E. Alpaydin

Applications• Recommend friends/dates/ • Detect anomalies in machine products output• Classify content into • Ranking search results predeﬁned groups • Fraud detection• Find similar content based on object properties • Spam detection• Find associations/patterns in • Medical diagnostics actions/behaviors • Translators• Identify key topics in large collections of text • Much more¡

Math• Stadistics• Discrete Math• Linear algebra• Probability

Starting with ML• Get your data• Decide on your features per your algorithm• Prep the data • Different approaches for different algorithms• Run your algorithm(s) • Lather, rinse, repeat• Validate your results • Smell test, A/B testing

Apache Mahout• Machine Learning library. Platform?• Extensible, we can use our own algorithm.• Hadoop support• 2005. Taste Framework• 2008. Included in Lucene

Scalability• Huge amount of data, growing every second¡• Be as fast and efﬁcient as possible given the intrinsic design of the algorithm • Some algorithms won’t scale to massive machine clusters • Others ﬁt logically on a Map Reduce framework like Apache Hadoop • Still others will need alternative distributed programming models • Be pragmatic• Most Mahout implementations are Map Reduce enabled

Who uses Mahout?

Components• Recommender Engines (collaborative ﬁltering, content-based)• Clustering• Classiﬁcation

When to use?• Recommendation • Rank large datasets• Clustering • Group your data• Classiﬁcation • Train me to think like you

Recommenders• Given a data set. Make a recomendation. • Item recomendation (Book, Movie, etc)• Ranking based• Recomendations • User based • Item based• knowledge of user’s relationships to items (user preferences)

Colaborative ﬁltering• User based• Item based• Both techniques require no knowledge of the properties of the items themselves.• Item Type is irrelevant. Apache Mahout is happy

Content based• Domain-speciﬁc approaches• Hard to meaningfully codify into a framework• We are responsables of choosing which items attributes to use.• Apache Mahout can’t handle this out-of- the-box, but can built on top.

Making recommendations • What we need? • Input data • Neighborhood • Similarity

Input Data• In Mahout terms: Preferences• A preference contains: • User ID • Item ID • Preference value • Example: • 1,101,5.0 • USER ID: 1, ITEM ID: 101, PrefValue: 5.0

NeighborhoodNearest N Users Threshold

Similarity

Clustering• Surface naturally occurring groups of data• A notion of similarity (and dissimilarity)• Algorithms do not require training• Stopping condition - iterate until close enough

Clustering• Document level • Group documents based on a notion of similarity • K-Means, Fuzzy K-Means, Dirichlet, Canopy, Mean-Shift • Distance Measures • Manhattan, Euclidean, other• Topic Modeling • Cluster words across documents to identify topics • Latent Dirichlet Allocation

Classiﬁcation• Require training (supervised)• Make a single decision with a very limited set of outcomes• Typical answers naturally ﬁt into categories

Classiﬁcation samples• Credit card fraud prediction• Customer attrition• Diabetes detector• Search Engine

Mahout/Hadoop• For large data sets• Online• Ofﬂine (Hadoop prefered)• You can build your solution with Mahout• Take a look into Weka • http://www.cs.waikato.ac.nz/ml/weka/

Resources

Join us¡• GIAMA. • Agustin Ramos iniciative

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Machine Learning & Apache Mahout

by Domingo Suárez Torres on Jun 21, 2012

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