The document provides an introduction to machine learning with Mahout. It discusses machine learning concepts and algorithms like clustering, classification, and recommendation. It introduces Hadoop as a framework for distributed processing of big data and Mahout as an open-source library for machine learning algorithms on Hadoop. The document demonstrates how to run recommendation algorithms and clustering algorithms using Mahout on local machines or cloud platforms like Amazon EC2 and EMR. It also discusses preprocessing text data and classifiers.

1. Introduction to
machine learning
with mahout
John Ternent
@jaternent
Orlando Data Science – www.orlandods.com
May 13, 2014

3. Welcome!
Updates

4. Social Media
 Facebook.com/orlandodata
 Twitter.com/orlandodata
 LinkedIn

5. OrlandoDS.com
 Social Network
 Forum
 Articles and Content
 And More
 Send articles to: scott@orlandods.com

6. Orlando Wiki
 Completely Open
 Aggregate Learning Resources!
 Go NUTS

7. May 28th Event
 Full Sail, UCF, and Florida Polytechnic
 Submit Your Questions! @orlandodata

8. Member Survey
 Need n=30!!!
 OrlandoDS.com/member-survey
 OR: find it in our past meetup
announcements

9. Learn Hadoop
 First Class: June 3rd.
 Location: Here

10. Future Plans
 Establish Non-Profit
 Increase Global Following
 Become Strong Networking and
Education Resource for YOU

11. A (very) little bit about
me…
 Consultant (Management & Technology)
 Open Source Evangelist
 Full-spectrum data nerd

12. A little about you!
 Rate yourself (1 – 10) on Mahout
 Rate yourself (1 – 10) on Machine
Learning/Data Mining
 Rate yourself (1 – 10) on Big
Data/Hadoop
 Please wait… optimizing presentation…

13. A computer program is said to learn from
experience E with respect to some class of
tasks T and performance measure P, if its
performance at tasks in T, as measured by
P, improves with experience E.
-- Tom M. Mitchell, 1997
Data mining is defined as the process of
discovering patterns in data. The process
must be automatic or (more usually)
semiautomatic. The patterns discovered must
be meaningful in that they lead to … an
economic advantage.
-- Ian H. Witten & Eibe Frank, 2005

14. If you’re in academia, you call it “machine
learning.” If you’re in business, you call it
“data mining.”
 Mark Hall
I create or
improve general
purpose
algorithms for
machine
learning
I use multiple
machine
learning
algorithms for
practical data
discovery
Source : xkcd Source : xkcd

15. Machine Learning Uses
Clustering
Classification
Recommendation

16. Machine Learning
Algorithms
 Regression
 K-means Clustering
 K-NN
 CART
 Neural Networks
 Support Vector Machines
 Association Rules
 Principal Component Analysis
 Singular Value Decomposition
 Ensemble Methods
 Naïve Bayes
 …

17. Real-World Applications
 Recommender Systems
 Image recognition
 Signal Processing
 Propensity to buy/churn
 Fraud analysis
 Text analytics
 Spam filtering
 Forecasting methods
 Revenue management
 …

18. The Problem … and Opportunity
Big Data™
If you have to choose, having more data does indeed trump a
better algorithm. However, what is better than just having
more data on its own is also having an algorithm that
annotates the data with new linkages and statistics which alter
the underlying data asset.”
- Omar Tawakol
Weka Explorer can handle ~1M instances, 25 attributes (50
MB file)
- Ian Witten

19. Potential Solutions
 Expand RAM
 Use incremental algorithms
 Use distributable algorithms
Scale
Up
Scale Out

20. Hadoop in 30 seconds
Input
Input
Input
Input
Input
Input
Input
Map (K,V)
Map (K,V)
Map (K,V)
Map (K,V)
Shuffle
/ Sort
Reduce
Reduce
Reduce
Output
Output
Output

21. Finally -- Mahout
 A Java-based library of machine
learning algorithms designed to support
distributed processing
 Initially on MapReduce, now leaning
heavily towards Spark
 Primarily focused on Recommenders,
Clustering, and Classification spaces.

22. Running Mahout
 Locally – download mahout distro.
/bin/mahout is the wrapper script, default shows all
the example programs available.
Lots of tools included to convert data into vector
formats and pre-process text, worth a look
 Amazon EC2
Configure stack from scratch on EC2 servers
 Amazon EMR
Quicker start, a lot of the build is already optimized
for MapReduce jobs, just add Mahout as a custom
jar and pass the script as a parameter

23. Running Recommenders
 Multiple Recommender Algorithms
User-based
Item-based
 A Recommender Needs:
DataModel (e.g. FileDataModel)
Similarity driver (PearsonCorrelationSimilarity)
Neighborhood (NearstNUserNeighborhood,
ThresholdUserNeighborhood)
Recommender

24. Running Recommenders
 Tip : If you have no preferences, there
are Boolean equivalents of the
recommender classes
 Evaluate user vs. item similarities
 Example

25. Clustering Algorithms
 To cluster you need:
Location in n-dimensional space
Distance metric
Threshold
 K-means
 Canopy
 Dirichlet
 Fuzzy K-means
 Spectral Clustering

26. Clustering

27. Clustering Text
 Identify k topics in a document corpus
 Requires conversion of text into vector
 Lucene utilities are available to vectorize
text and apply stop-word or weighting
criteria.
 Seqdirectory – from a directory of text
files
 Lucene.vector – from a Lucene index

28. Classifiers
 NaïveBayes
 RandomForests
 LogisticRegression (SGD)
 HiddenMarkov
 Example : 20 Newsgroups

29. Sidebar : Risks of Big Data
Unsupervised Learning