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Introducing Apache Mahout. Scalable Machine Learning for All! Grant Ingersoll Lucid Imagination. Overview. What is Machine Learning? Mahout. Definition. “Machine Learning is programming computers to optimize a performance criterion using example data or past experience” - PowerPoint PPT Presentation
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Introducing Apache Mahout
Scalable Machine Learning for All!
Grant Ingersoll
Lucid Imagination
Overview
• What is Machine Learning?
• Mahout
Definition• “Machine Learning is programming
computers to optimize a performance criterion using example data or past experience”– Intro. To Machine Learning by E.
Alpaydin
• Subset of Artificial Intelligence– Many other fields: comp sci., biology,
math, psychology, etc.
Types• Supervised
– Using labeled training data, create function that predicts output of unseen inputs
• Unsupervised– Using unlabeled data, create function
that predicts output
• Semi-Supervised– Uses labeled and unlabeled data
Characterizations
• Lots of Data
• Identifiable Features in that Data
• Too big/costly for people to handle– People still can help
Clustering
• Unsupervised
• Find Natural Groupings– Documents– Search Results– People– Genetic traits in groups– Many, many more uses
Example: Clustering
Google News
Collaborative Filtering
• Unsupervised
• Recommend people and products– User-User
• User likes X, you might too
– Item-Item• People who bought X also bought Y
Example: Collab Filtering
Amazon.com
Classification/Categorization
• Many, many types
• Spam Filtering
• Named Entity Recognition
• Phrase Identification
• Sentiment Analysis
• Classification into a Taxonomy
Example: NER
NER?
Excerpt from Yahoo News
Example: Categorization
Info. Retrieval
• Learning Ranking Functions
• Learning Spelling Corrections
• User Click Analysis and Tracking
Other
• Image Analysis
• Robotics
• Games
• Higher level natural language processing
• Many, many others
What is Apache Mahout?
• A Mahout is an elephant trainer/driver/keeper, hence…
+Machine Learning
=
(and other distributed techniques)
What?
• Hadoop brings:– Map/Reduce API– HDFS– In other words, scalability and fault-
tolerance
• Mahout brings:– Library of machine learning algorithms– Examples
Why Mahout?• Many Open Source ML libraries either:
– Lack Community
– Lack Documentation and Examples
– Lack Scalability
– Lack the Apache License ;-)
– Or are research-oriented
Why Mahout?• Intelligent Apps are the Present and
Future
• Thus, Mahout’s Goal is:– Scalable Machine Learning with Apache
License
Current Status• What’s in it:
– Simple Matrix/Vector library– Taste Collaborative Filtering– Clustering
• Canopy/K-Means/Fuzzy K-Means/Mean-shift/Dirichlet
– Classifiers• Naïve Bayes• Complementary NB
– Evolutionary• Integration with Watchmaker for fitness function
How?
• Examples– Taste– Clustering– Classification– Evolutionary
Taste: Movie Recommendations
• Given ratings by users of movies, recommend other movies
• http://lucene.apache.org/mahout/taste.html#demo
Taste Demo
• http://localhost:8080/mahout-taste-webapp/RecommenderServlet?userID=12&debug=true
• http://localhost:8080/mahout-taste-webapp/RecommenderServlet?userID=43&debug=true
Clustering: Synthetic Control Data
• http://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series
• Each clustering impl. has an example Job for running in <MAHOUT_HOME>/examples– o.a.mahout.clustering.syntheticcontrol.*
• Outputs clusters…
Classification: NB and CNB Examples
• 20 Newsgroups– http://cwiki.apache.org/confluence/
display/MAHOUT/TwentyNewsgroups
• Wikipedia– http://cwiki.apache.org/confluence/
display/MAHOUT/WikipediaBayesExample
Evolutionary
• Traveling Salesman– http://cwiki.apache.org/confluence/
display/MAHOUT/Traveling+Salesman
• Class Discovery– http://cwiki.apache.org/confluence/
display/MAHOUT/Class+Discovery
What’s Next?• More Examples• Winnow/Perceptron (MAHOUT-85)• Text Clustering• Association Rules (MAHOUT-108)• Logistic Regression• Solr Integration (SOLR-769)• GSOC
When, Who• When? Now!
– Mahout is growing
• Who? You!– We want programmers who:
• Are comfortable with math• Like to work on hard problems
– We want others to:• Kick the tires
Where?
• http://lucene.apache.org/mahout– Hadoop - http://hadoop.apache.org
• http://cwiki.apache.org/MAHOUT
• mahout-{user|dev}@lucene.apache.org– http://www.lucidimagination.com/search/p:mahout
Resources
• “Programming Collective Intelligence” by Segaran
• “Data Mining - Practical Machine Learning Tools and Techniques” by Witten and Frank
• “Taming Text” by Ingersoll and Morton