BigML is the first Machine Learning service offering Association Discovery on the cloud! With these slides you can learn how to use Association Discovery and other new features such as Partial Dependence Plots, Logistic Regression, Correlations, Statistical Tests and Flatline Editor.

1. Introducing
Association Discovery
BigML 2015 Fall Release

2. BigML Inc Fall 2015 Release 2
Today’s Webinar
•Speaker:
•Poul Petersen, CIO
•Moderator:
•Atakan Ce>nsoy, VP Predic>ve Applica>ons
•Enter ques>ons into chat box – we’ll answer
some via text; others at the end of the session
•email: info@bigml.com
•TwiPer: @bigmlcom

3. BigML Inc Fall 2015 Release 3
Associa1on Discovery
Algorithm
“Magnum Opus” from
Geoff Webb
Unsupervised Learning:
unlabelled data
Learning Task:
Find “interes1ng” rela1ons
between variables.

4. BigML Inc Fall 2015 Release
Decision Trees
Bagging
Decision Forest
4
BigML Workflow
MODEL
DATASET
CLUSTER
ANOMALY
ASSOCIATION
SOURCE
K-Means
G-Means
Isola>on Forest
Magnum Opus

5. BigML Inc Fall 2015 Release 5
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
Clustering
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
Anomaly Detec1on
similar
unusual
Unsupervised Learning

6. BigML Inc Fall 2015 Release
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
6
{customer = Bob, account = 3421} zip = 46140
Rules:
{class = gas} amount > 80
Associa1on Rules

7. BigML Inc Fall 2015 Release
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
7
{customer = Bob, account = 3421} zip = 46140
Rules:
{class = gas} amount > 80
Antecedent Consequent
Associa1on Rules

8. BigML Inc Fall 2015 Release 8
Use Cases
• Market Basket Analysis
• Web usage paPerns
• Intrusion detec>on
• Fraud detec>on
• Bioinforma>cs
• Medical risk factors

9. BigML Inc Fall 2015 Release 9
Market Basket Analysis
• Dataset of 9,834 grocery cart transac>ons
• Each row is a list of all items in a cart at checkout
GOAL: Discover “interes1ng” rules about what store items
are typically purchased together.

10. BigML Inc Fall 2015 Release 10
Associa1on Metrics
Instances
A
C
Coverage
Percentage of instances which
match antecedent “A”

11. BigML Inc Fall 2015 Release 11
Associa1on Metrics
Instances
A
C
Support
Percentage of instances which
match antecedent “A” and
Consequent “C”

12. BigML Inc Fall 2015 Release
Confidence
Percentage of instances in the
antecedent which also contain
the consequent.
Support
Coverage
12
Associa1on Metrics
Instances
A
C

13. BigML Inc Fall 2015 Release
C
Instances
A
C
A
Instances
C
Instances
A
13
Associa1on Metrics
Instances
A
C
0% 100%
Instances
A
C
Confidence
A never
implies C
A some1mes
implies C
A always
implies C

14. BigML Inc Fall 2015 Release
LiO
Ra>o of observed support to
support if A and C were
sta>s>cally independent.
Support == Confidence
p(A) * p(C) p(C)
14
Associa1on Metrics
Independent
A
C
C
Observed
A

15. BigML Inc Fall 2015 Release
C
Observed
A
15
Associa1on Metrics
Observed
A
C
< 1 > 1
Independent
A
C
Lift = 1
Nega>ve
Correla>on
No Associa>on
Posi>ve
Correla>on
Independent
A
C
Independent
A
C
Observed
A
C

16. BigML Inc Fall 2015 Release 16
Associa1on Metrics
Independent
A
C
C
Observed
A
Leverage
Difference of observed support
and support if A and C were
sta>s>cally independent.
Support - [ p(A) * p(C) ]

17. BigML Inc Fall 2015 Release
C
Observed
A
17
Associa1on Metrics
Observed
A
C
< 0 > 0
Independent
A
C
Leverage = 0
Nega>ve
Correla>on
No Associa>on
Posi>ve
Correla>on
Independent
A
C
Independent
A
C
Observed
A
C
-1… …1

18. BigML Inc Fall 2015 Release 18
GOAL: Find general rules that indicate diabetes.
• Dataset of diagnos>c measurements of 768
pa>ents.
• Each pa>ent labelled True/False for diabetes.
Medical Risk

19. BigML Inc Fall 2015 Release 19
Medical Risk
Associa1on Rule
If plasma glucose > 146
then diabetes = TRUE
Decision Tree
If plasma glucose > 155
and bmi > 29.32
and diabetes pedigree > 0.32
and insulin <= 629
and age <= 44
then diabetes = TRUE

20. BigML Inc Fall 2015 Release 20
Par1al Dependence Plots
Visualize Ensembles

21. BigML Inc Fall 2015 Release 21
Flatline Editor
hPps://github.com/bigmlcom/flatline

22. BigML Inc Fall 2015 Release
Decision Trees
Bagging
Decision Forest
22
BigML Workflow
MODEL
DATASET
CLUSTER
ANOMALY
ASSOCIATION
SOURCE
K-Means
G-Means
Isola>on Forest
Magnum Opus
DATASET
Flatline
Flatline Editor

23. BigML Inc Fall 2015 Release 23
Logis1c Regression
DATASET LOGISTIC REGRESSION
• Classifica>on algorithm
• Categorical: one-hot encoded
• Text: mapped to token freq
• Bindings support local model
• I1/I2 regulariza>on
• Currently API only
hPps://bigml.com/developers/logis>cregressions

24. BigML Inc Fall 2015 Release
Decision Trees
Bagging
Decision Forest
Logis>c Regression
24
BigML Workflow
MODEL
DATASET
CLUSTER
ANOMALY
ASSOCIATION
SOURCE
K-Means
G-Means
Isola>on Forest
Magnum Opus
DATASET
Flatline
Flatline Editor

25. BigML Inc Fall 2015 Release 25
BigML Classifiers
Advantages Disadvantages
Single Tree
easy to interpret
robust to missing data
overfiong
Ensemble
top performer
robust to missing data
hard to interpret
Logis1c Regression
robust to noise
outputs probability
no missing data
hard to interpret

26. BigML Inc Fall 2015 Release
Decision Trees
Bagging
Decision Forest
Logis>c Regression
26
BigML Workflow
MODEL
DATASET
CLUSTER
ANOMALY
ASSOCIATION
SOURCE
K-Means
G-Means
Isola>on Forest
Magnum Opus
Sta>s>cal Tests
Correla>ons
STATS
DATASET
Flatline
Flatline Editor

27. BigML Inc Fall 2015 Release 27
Correla1ons
DATASET CORRELATION
• Pearson Coefficient
• Spearman Coefficient
• Chi-Square
• Cramér's V
• Tschuprow's T
• One-way ANOVA
hPps://bigml.com/developers/correla>ons

28. BigML Inc Fall 2015 Release 28
Sta1s1cal Tests
DATASET STATISTICAL TESTS
• Benford’s Law
• Anderson-Darling
• Jarque-Bera
• Z-score
• Grubbs
hPps://bigml.com/developers/sta>s>caltests

29. BigML Inc Fall 2015 Release
Decision Trees
Bagging
Decision Forest
Logis>c Regression
29
BigML Workflow
MODEL
DATASET
CLUSTER
ANOMALY
ASSOCIATION
SOURCE
K-Means
G-Means
Isola>on Forest
Magnum Opus
Sta>s>cal Tests
Correla>ons
STATS
DATASET
Flatline
Flatline Editor

30. BigML Inc Fall 2015 Release 30
Q&A
•Ask ques1ons and get a Free BigML T-shirt!
•All demonstrated features are immediately available to all users
including:
•All subscrip1on plans
•Virtual Private Cloud (VPC) customers
•On-premise implementa1ons.
•Documenta1on@ hRps://bigml.com/releases

31. BigML Inc Fall 2015 Release 31
FEEDBACK
@bigmlcomTWITTER
info@bigml.com
Get Started Today!
RESOURCES
Join us for future
webinars & hangouts
OFFICE
HOURS
Every Wednesday
9:30am Pacific Time