This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.

1. Deriving Knowledge from Data at Scale

2. Deriving Knowledge from Data at Scale
Models in Production

3. Deriving Knowledge from Data at Scale
Putting an ML Model into Production
• A/B Testing

4. Deriving Knowledge from Data at Scale
Controlled Experiments in One Slide
Concept is Trivial
• Must run statistical tests to confirm differences are not due to chance
• Best scientific way to prove causality, i.e., the changes in metrics are
caused by changes introduced in the treatment(s)

5. Deriving Knowledge from Data at Scale
Best Practice: A/A Test
Run A/A tests
before

6. Deriving Knowledge from Data at Scale
Best Practice: Ramp-up
Ramp-up

7. Deriving Knowledge from Data at Scale
Best Practice: Run Experiments at 50/50%

8. Deriving Knowledge from Data at Scale
Cost based learning

9. Deriving Knowledge from Data at Scale
Imbalanced Class Distribution & Error Costs
WEKA cost sensitive learning
weighting method
false negatives, FN
try to avoid
false negatives

10. Deriving Knowledge from Data at Scale
Imbalanced Class Distribution
WEKA cost sensitive learning
Preprocess Classify
meta.CostSensitiveClassifier
set the FN to 10.0 FP to 1.0
tries to optimize accuracy or error can be cost-sensitive
decision trees rule learner

11. Deriving Knowledge from Data at Scale
Imbalanced Class Distribution
WEKA cost sensitive learning

12. Deriving Knowledge from Data at Scale

13. Deriving Knowledge from Data at Scale
curated
completely specify a problem measure progress
paired with a metric target SLAs score
board

14. Deriving Knowledge from Data at Scale
This isn’t easy…
• Building high quality gold sets is a challenge.
• It is time consuming.
• It requires making difficult and long lasting
choices, and the rewards are delayed…

15. Deriving Knowledge from Data at Scale
enforce a few principles
1. Distribution parity
2. Testing blindness
3. Production parity
4. Single metric
5. Reproducibility
6. Experimentation velocity
7. Data is gold

16. Deriving Knowledge from Data at Scale
• Test set blindness
• Reproducibility and Data is gold
• Experimentation velocity

17. Deriving Knowledge from Data at Scale
Building Gold sets is hard work. Many common and avoidable mistakes are
made. This suggests having a checklist. Some questions will be trivial to
answer or not applicable, some will require work…
1. Metrics: For each gold set, chose one (1) metric. Having two metrics on the same
gold set is a problem (you can’t optimize both at once).
2. Weighting/Slicing: Not all errors are equal. This should be reflected in the metric, not
through sampling manipulation. Having the weighting in the metric has two
advantages: 1) it is explicitly documented and reproducible in the form of a metric
algorithm, and 2) production, train, and test sets results remain directly comparable
(automatic testing).
3. Yardstick(s): Define algorithms and configuration parameters for public yardstick(s).
There could be more than one yardstick. A simple yardstick is useful for ramping up.
Once one can reproduce/understand the simple yardstick’s result, it becomes easier
to improve on the latest “production” yardstick. Ideally yardsticks come with
downloadable code. The yardsticks provide a set of errors that suggests where
innovation should happen.

18. Deriving Knowledge from Data at Scale
4. Sizes and access: What are the set sizes? Each size corresponds to an innovation
velocity and a level of representativeness. A good rule of thumb is 5X size ratios
between gold sets drawn from the same distribution. Where should the data live? If
on a server, some services are needed for access and simple manipulations. There
should always be a size that is downloadable (< 1GB) to a desktop for high velocity
innovation.
5. Documentation and format: Create a format/API for the data. Is the data
compressed? Provide sample code to load the data. Document the format. Assign
someone to be the curator of the gold set.

19. Deriving Knowledge from Data at Scale
6. Features: What (gold) features go in the gold sets? Features must be pickled for result
to be reproducible. Ideally, we would have 2, and possibly 3 types of gold sets.
a. One set should have the deployed features (computed from the raw data). This provides the
production yardstick.
b. One set should be Raw (e.g. contains all information, possibly through tables). This allows
contributors to create features from the raw data to investigate its potential compared to existing
features. This set has more information per pattern and a smaller number of patterns.
c. One set should have an extended number of features. The additional features may be “building
blocks”, features that are scheduled to be deployed next, or high potential features. Moving some
features to a gold set is convenient if multiple people are working on the next generation. Not all
features are worth being in a gold set.
7. Feature optimization sets: Does the data require feature optimization? For instance,
an IP address, a query, or a listing id may be features. But only the most frequent 10M
instances are worth having specific trainable parameters. A pass over the data can
identify the top 10M instance. This is a form of feature optimization. Identifying these
features does not require labels. If a form of feature optimization is done, a separate
data set (disjoint from the training and test set) must be provided.

20. Deriving Knowledge from Data at Scale
8. Stale rate, optimization, monitoring: How long does the set stay current? In many
cases, we hide the fact that the problem is a time series even though the goal is to
predict the future and we know that the distribution is changing. We must quantify
how much a distribution changes over a fixed period of time. There are several ways
to mitigate the changing distribution problem:
a. Assume the distribution is I.I.D. Regularly re-compute training sets and Gold sets. Determine the
frequency of re-computation, or set in place a system to monitor distribution drifts (monitor KPI
changes while the algorithm is kept constant).
b. Decompose the model along “distribution (fast) tracking parameters” and slow tracking parameters.
The fast tracking model may be a simple calibration with very few parameters.
c. Recast the problem as a time series problem: patterns are (input data from t-T to t-1, prediction at
time t). In this space, the patterns are much larger, but the problem is closer to being I.I.D.
9. The gold sets should have information that reveal the stale rate and allows algorithms
to differentiate themselves based on how they degrade with time.

21. Deriving Knowledge from Data at Scale
10. Grouping: Should the patterns be grouped? For example in handwriting, examples are
grouped per writer. A set built by shuffling the words is misleading because training
and testing would have word examples for the same writer, which makes
generalization much easier. If the words are grouped per writers, then a writer is
unlikely to appear in both training and test set, which requires the system to generalize
to never seen before handwriting (as opposed to never seen before words). Do we
have these type of constraints? Should we group per advertisers, campaign, users to
generalize across new instances of these entities (as opposed to generalizing to new
queries)? ML requires training and testing to be drawn from the same distribution.
Drawing duplicates is not a problem. Problems arise when one partially draw
examples from the same entity on both training and testing on a small set of entities.
This breaks the IID assumption and makes the generalization on the test set much
easier than it actually is.
11. Sampling production data: What strategy is used for sampling? Uniform? Are any of
the following filtered out: fraud, bad configurations, duplicates, non-billable, adult,
overwrites, etc? Guidance: use the production sameness principle.

22. Deriving Knowledge from Data at Scale
11. Unlabeled set: If the number of labeled examples is small, a large data set of
unlabeled data with the same distribution should be collected and be made a gold
set. This enables the discovery of new features using intermediate classifiers and
active labeling.

23. Deriving Knowledge from Data at Scale
GreatestChallengeinMachineLearning

24. Deriving Knowledge from Data at Scale
gender age smoker eye
color
male 19 yes green
female 44 yes gray
male 49 yes blue
male 12 no brown
female 37 no brown
female 60 no brown
male 44 no blue
female 27 yes brown
female 51 yes green
female 81 yes gray
male 22 yes brown
male 29 no blue
lung
cancer
no
yes
yes
no
no
yes
no
no
yes
no
no
no
male 77 yes gray
male 19 yes green
female 44 no gray
yes
no
no
Train
ML Model

25. Deriving Knowledge from Data at Scale
The greatest challenge in Machine Learning?
Lack of Labelled Training Data…
What to Do?
• Controlled Experiments – get feedback from user to serve as labels;
• Mechanical Turk – pay people to label data to build training set;
• Ask Users to Label Data – report as spam, ‘hot or not?’, review a product,
observe their click behavior (ad retargeting, search results, etc).

26. Deriving Knowledge from Data at Scale
Whatifyoucan'tgetlabeledTrainingData?
Traditional Supervised Learning
• Promotion on bookseller’s web page
• Customers can rate books.
• Will a new customer like this book?
• Training set: observations on previous customers
• Test set: new customers
Whathappensif onlyfew customers rate a book?
Age Income LikesBook
24 60K +
65 80K -
60 95K -
35 52K +
20 45K +
43 75K +
26 51K +
52 47K -
47 38K -
25 22K -
33 47K +
Age Income LikesBook
22 67K ?
39 41K ?
Age Income LikesBook
22 67K +
39 41K -
Model
Test Data
Prediction
Training Data
Attributes
Target
Label
© 2013 Datameer, Inc. All rights reserved.
Age Income LikesBook
24 60K +
65 80K -
60 95K -
35 52K +
20 45K +
43 75K +
26 51K +
52 47K -
47 38K -
25 22K -
33 47K +
Age Income LikesBook
22 67K ?
39 41K ?
Age Income LikesBook
22 67K +
39 41K -

27. Deriving Knowledge from Data at Scale
Semi-SupervisedLearning
Can we makeuse of the unlabeled data?
In theory: no
... but we can make assumptions
PopularAssumptions
• Clustering assumption
• Low density assumption
• Manifold assumption

28. Deriving Knowledge from Data at Scale
TheClusteringAssumption
Clustering
• Partition instances into groups (clusters) of similar
instances
• Many different algorithms: k-Means, EM, etc.
Clustering Assumption
• The two classification targets are distinct clusters
• Simple semi-supervised learning: cluster, then
perform majority vote

29. Deriving Knowledge from Data at Scale
TheClusteringAssumption
Clustering
• Partition instances into groups (clusters) of similar
instances
• Many different algorithms: k-Means, EM, etc.
Clustering Assumption
• The two classification targets are distinct clusters
• Simple semi-supervised learning: cluster, then
perform majority vote

30. Deriving Knowledge from Data at Scale
TheClusteringAssumption
Clustering
• Partition instances into groups (clusters) of similar
instances
• Many different algorithms: k-Means, EM, etc.
Clustering Assumption
• The two classification targets are distinct clusters
• Simple semi-supervised learning: cluster, then
perform majority vote

31. Deriving Knowledge from Data at Scale
TheClusteringAssumption
Clustering
• Partition instances into groups (clusters) of similar
instances
• Many different algorithms: k-Means, EM, etc.
Clustering Assumption
• The two classification targets are distinct clusters
• Simple semi-supervised learning: cluster, then
perform majority vote

32. Deriving Knowledge from Data at Scale
Generative Models
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

33. Deriving Knowledge from Data at Scale
GenerativeModels
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

34. Deriving Knowledge from Data at Scale
GenerativeModels
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

35. Deriving Knowledge from Data at Scale
Generative Models
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

36. Deriving Knowledge from Data at Scale
Generative Models
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

37. Deriving Knowledge from Data at Scale
Generative Models
Mixture of Gaussians
• Assumption: the data in each cluster is generated
by a normal distribution
• Find most probable location and shape of clusters
given data
Expectation-Maximization
• Two step optimization procedure
• Keeps estimates of cluster assignment probabilities
for each instance
• Might converge to local optimum

38. Deriving Knowledge from Data at Scale
BeyondMixtures of Gaussians
Expectation-Maximization
• Can be adjusted to all kinds of mixture models
• E.g. use Naive Bayes as mixture model for text classification
Self-Training
• Learn model on labeled instances only
• Apply model to unlabeled instances
• Learn new model on all instances
• Repeat until convergence

39. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Assumption
• The area between the two classes has low density
• Does not assume any specific form of cluster
Support Vector Machine
• Decision boundary is linear
• Maximizes margin to closest instances

40. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Assumption
• The area between the two classes has low density
• Does not assume any specific form of cluster
Support Vector Machine
• Decision boundary is linear
• Maximizes margin to closest instances

41. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Assumption
• The area between the two classes has low density
• Does not assume any specific form of cluster
Support Vector Machine
• Decision boundary is linear
• Maximizes margin to closest instances

42. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Semi-Supervised SVM
• Minimize distance to labeled and
unlabeled instances
• Parameter to fine-tune influence of
unlabeled instances
• Additional constraint: keep class balance correct
Implementation
• Simple extension of SVM
• But non-convex optimization problem

43. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Semi-Supervised SVM
• Minimize distance to labeled and
unlabeled instances
• Parameter to fine-tune influence of
unlabeled instances
• Additional constraint: keep class balance correct
Implementation
• Simple extension of SVM
• But non-convex optimization problem

44. Deriving Knowledge from Data at Scale
TheLow DensityAssumption
Semi-Supervised SVM
• Minimize distance to labeled and
unlabeled instances
• Parameter to fine-tune influence of
unlabeled instances
• Additional constraint: keep class balance correct
Implementation
• Simple extension of SVM
• But non-convex optimization problem

45. Deriving Knowledge from Data at Scale
Semi-Supervised SVM
Stochastic Gradient Descent
• One run over the data in random order
• Each misclassified or unlabeled instance moves
classifier a bit
• Steps get smaller over time
Implementation on Hadoop
• Mapper: send data to reducer in random order
• Reducer: update linear classifier for unlabeled
or misclassified instances
• Many random runs to find best one

46. Deriving Knowledge from Data at Scale
Semi-Supervised SVM
Stochastic Gradient Descent
• One run over the data in random order
• Each misclassified or unlabeled instance moves
classifier a bit
• Steps get smaller over time
Implementation on Hadoop
• Mapper: send data to reducer in random order
• Reducer: update linear classifier for unlabeled
or misclassified instances
• Many random runs to find best one

47. Deriving Knowledge from Data at Scale
Semi-Supervised SVM
Stochastic Gradient Descent
• One run over the data in random order
• Each misclassified or unlabeled instance moves
classifier a bit
• Steps get smaller over time
Implementation on Hadoop
• Mapper: send data to reducer in random order
• Reducer: update linear classifier for unlabeled
or misclassified instances
• Many random runs to find best one

48. Deriving Knowledge from Data at Scale
Semi-Supervised SVM
Stochastic Gradient Descent
• One run over the data in random order
• Each misclassified or unlabeled instance moves
classifier a bit
• Steps get smaller over time
Implementation on Hadoop
• Mapper: send data to reducer in random order
• Reducer: update linear classifier for unlabeled
or misclassified instances
• Many random runs to find best one

49. Deriving Knowledge from Data at Scale
Semi-Supervised SVM
Stochastic Gradient Descent
• One run over the data in random order
• Each misclassified or unlabeled instance moves
classifier a bit
• Steps get smaller over time
Implementation on Hadoop
• Mapper: send data to reducer in random order
• Reducer: update linear classifier for unlabeled
or misclassified instances
• Many random runs to find best one

50. Deriving Knowledge from Data at Scale
TheManifoldAssumption
The Assumption
• Training data is (roughly) contained in a low
dimensional manifold
• One can perform learning in a more meaningful
low-dimensional space
• Avoids curse of dimensionality
Similarity Graphs
• Idea: compute similarity scores between instances
• Create network where the nearest
neighbors are connected

51. Deriving Knowledge from Data at Scale
TheManifoldAssumption
The Assumption
• Training data is (roughly) contained in a low
dimensional manifold
• One can perform learning in a more meaningful
low-dimensional space
• Avoids curse of dimensionality
Similarity Graphs
• Idea: compute similarity scores between instances
• Create a network where the nearest neighbors are
connected

52. Deriving Knowledge from Data at Scale
TheManifoldAssumption
The Assumption
• Training data is (roughly) contained in a low
dimensional manifold
• One can perform learning in a more
meaningful low-dimensional space
• Avoids curse of dimensionality
SimilarityGraphs
• Idea: compute similarity scores between instances
•
Create network where the nearest neighbors
are connected

53. Deriving Knowledge from Data at Scale
Label Propagation
MainIdea
• Propagate label information to neighboring instances
• Then repeat until convergence
• Similar to PageRank
Theory
• Known to converge under weak conditions
• Equivalent to matrix inversion

54. Deriving Knowledge from Data at Scale
Label Propagation
MainIdea
• Propagate label information to neighboring instances
• Then repeat until convergence
• Similar to PageRank
Theory
• Known to converge under weak conditions
• Equivalent to matrix inversion

55. Deriving Knowledge from Data at Scale
Label Propagation
MainIdea
• Propagate label information to neighboring instances
• Then repeat until convergence
• Similar to PageRank
Theory
• Known to converge under weak conditions
• Equivalent to matrix inversion

56. Deriving Knowledge from Data at Scale
Label Propagation
MainIdea
• Propagate label information to neighboring instances
• Then repeat until convergence
• Similar to PageRank
Theory
• Known to converge under weak conditions
• Equivalent to matrix inversion

57. Deriving Knowledge from Data at Scale
Conclusion
Semi-Supervised Learning
• Only few training instances have labels
• Unlabeled instances can still provide valuable signal
Different assumptions lead to different approaches
• Cluster assumption: generative models
• Low density assumption: semi-supervised support vector machines
• Manifold assumption: label propagation

58. Deriving Knowledge from Data at Scale
10 Minute Break…

59. Deriving Knowledge from Data at Scale
Controlled Experiments

60. Deriving Knowledge from Data at Scale
• A
• B

61. Deriving Knowledge from Data at Scale
OEC
Overall Evaluation Criterion
Picking a good OEC is key

62. Deriving Knowledge from Data at Scale

63. Deriving Knowledge from Data at Scale
• Lesson #2: GET THE DATA

64. Deriving Knowledge from Data at Scale

65. Deriving Knowledge from Data at Scale
• Lesson #2: Get the data!

66. Deriving Knowledge from Data at Scale
Lesson #3: Prepare to be humbled
Left Elevator Right Elevator

67. Deriving Knowledge from Data at Scale
• Lesson #1
• Lesson #2
• Lesson #3
15% Bing

68. Deriving Knowledge from Data at Scale
• HiPPO stop the project
From Greg Linden’s Blog: http://glinden.blogspot.com/2006/04/early-amazon-shopping-cart.html

69. Deriving Knowledge from Data at Scale
TED talk

70. Deriving Knowledge from Data at Scale
• Must run statistical tests to confirm differences are not due to chance
• Best scientific way to prove causality, i.e., the changes in metrics are
caused by changes introduced in the treatment(s)

71. Deriving Knowledge from Data at Scale

72. Deriving Knowledge from Data at Scale
• Raise your right hand if you think A Wins
• Raise your left hand if you think B Wins
• Don’t raise your hand if you think they’re about the same
A B

73. Deriving Knowledge from Data at Scale
• A was 8.5% better

74. Deriving Knowledge from Data at Scale
A
B
Differences: A has taller search box (overall size is the same), has magnifying glass icon,
“popular searches”
B has big search button
• Raise your right hand if you think A Wins
• Raise your left hand if you think B Wins
• Don’t raise your hand if they are the about the same

75. Deriving Knowledge from Data at Scale

76. Deriving Knowledge from Data at Scale

77. Deriving Knowledge from Data at Scale
A B
• Raise your right hand if you think A Wins
• Raise your left hand if you think B Wins
• Don’t raise your hand if they are the about the same

78. Deriving Knowledge from Data at Scale
get the data prepare to be
humbled

79. Deriving Knowledge from Data at Scale
Any statistic that appears interesting is almost certainly a mistake
 If something is “amazing,” find the flaw!
 Examples
 If you have a mandatory birth date field and people think it’s
unnecessary, you’ll find lots of 11/11/11 or 01/01/01
 If you have an optional drop down, do not default to the first
alphabetical entry, or you’ll have lots jobs = Astronaut
 The previous Office example assumes click maps to revenue.
Seemed reasonable, but when the results look so extreme, find
the flaw (conversion rate is not the same; see why?)

80. Deriving Knowledge from Data at Scale
Data Trumps Intuition

81. Deriving Knowledge from Data at Scale
Sir Ken Robinson

82. Deriving Knowledge from Data at Scale
• OEC = Overall Evaluation Criterion

83. Deriving Knowledge from Data at Scale
• Controlled Experiments in one slide
• Examples: you’re the decision maker

84. Deriving Knowledge from Data at Scale
It is difficult to get a man to understand something when his
salary depends upon his not understanding it.
-- Upton Sinclair

85. Deriving Knowledge from Data at Scale
Hubris

86. Deriving Knowledge from Data at Scale
Cultural Stage 2
Insight through Measurement and Control
• Semmelweis worked at Vienna’s General Hospital, an
important teaching/research hospital, in the 1830s-40s
• In 19th-century Europe, childbed fever killed more than a million
women
• Measurement: the mortality rate for women giving birth was
• 15% in his ward, staffed by doctors and students
• 2% in the ward at the hospital, attended by midwives

87. Deriving Knowledge from Data at Scale
Cultural Stage 2
Insight through Measurement and Control
• He tried to control all differences
• Birthing positions, ventilation, diet, even the way laundry was done
• He was away for 4 months and death rate fell significantly when
he was away. Could it be related to him?
• Insight:
• Doctors were performing autopsies each morning on cadavers
• Conjecture: particles (called germs today) were being transmitted to
healthy patients on the hands of the physicians
• He experiments with cleansing agents
• Chlorine and lime was effective: death rate fell from 18% to 1%

88. Deriving Knowledge from Data at Scale
Semmelweis Reflex
• Semmelweis Reflex
2005 study: inadequate hand washing is one of the
prime contributors to the 2 million health-care-associated infections and
90,000 related deaths annually in the United States

89. Deriving Knowledge from Data at Scale
Fundamental Understanding

90. Deriving Knowledge from Data at Scale
Hubris
Measure and
Control
Accept Results
avoid
Semmelweis
Reflex
Fundamental
Understanding

91. Deriving Knowledge from Data at Scale
• Controlled Experiments in one slide
• Examples: you’re the decision maker
• Cultural evolution: hubris, insight through measurement,
Semmelweis reflex, fundamental understanding

92. Deriving Knowledge from Data at Scale

93. Deriving Knowledge from Data at Scale
• Real Data for the city of Oldenburg,
Germany
• X-axis: stork population
• Y-axis: human population
What your mother told you about babies and
storks when you were three is still not right,
despite the strong correlational “evidence”
Ornitholigische Monatsberichte 1936;44(2)

94. Deriving Knowledge from Data at Scale
Women have smaller palms and live 6 years longer
on average
But…don’t try to bandage your hands

95. Deriving Knowledge from Data at Scale
causal

96. Deriving Knowledge from Data at Scale
If you don't know where you are going, any road will take you there
—Lewis Carroll

97. Deriving Knowledge from Data at Scale

98. Deriving Knowledge from Data at Scale
before

99. Deriving Knowledge from Data at Scale

100. Deriving Knowledge from Data at Scale

101. Deriving Knowledge from Data at Scale
• Hippos kill more humans than any other (non-human) mammal (really)
• OEC
Get the data
• Prepare to be humbled
The less data, the stronger the opinions…

102. Deriving Knowledge from Data at Scale
Out of Class Reading
Eight (8) page conference paper
40 page journal version…

103. Deriving Knowledge from Data at Scale

104. Deriving Knowledge from Data at Scale
Course Project
Due Oct. 25th

105. Deriving Knowledge from Data at Scale
Open Discussion on
Course Project…

106. Deriving Knowledge from Data at Scale

107. Deriving Knowledge from Data at Scale

108. Deriving Knowledge from Data at Scale
Gallery of Experiments
Contributed by the community

109. Deriving Knowledge from Data at Scale
Azure Machine Learning Studio

110. Deriving Knowledge from Data at Scale
Sample
Experiments
To help you get started

111. Deriving Knowledge from Data at Scale
Experiment
Tools that you can use in your
experiment. For feature
selection, large set of machine
learning algorithms

112. Deriving Knowledge from Data at Scale

113. Deriving Knowledge from Data at Scale
Using
classificatio
n
algorithms
Evaluating
the model
Splitting to
Training
and Testing
Datasets
Getting
Data
For the
Experiment

114. Deriving Knowledge from Data at Scale
http://gallery.azureml.net/browse/?tags=[%22Azure%20ML%20Book%22

115. Deriving Knowledge from Data at Scale
Customer Churn Model

116. Deriving Knowledge from Data at Scale
Deployed web service endpoints
that can be consumed by applications
and for batch processing

117. Deriving Knowledge from Data at Scale

118. Deriving Knowledge from Data at Scale
Define
Objective
Access and
Understand the
Data
Pre-processing
Feature and/or
Target
construction
1. Define the objective and quantify it with a metric – optionally with constraints,
if any. This typically requires domain knowledge.
2. Collect and understand the data, deal with the vagaries and biases in the data
acquisition (missing data, outliers due to errors in the data collection process,
more sophisticated biases due to the data collection procedure etc
3. Frame the problem in terms of a machine learning problem – classification,
regression, ranking, clustering, forecasting, outlier detection etc. – some
combination of domain knowledge and ML knowledge is useful.
4. Transform the raw data into a “modeling dataset”, with features, weights,
targets etc., which can be used for modeling. Feature construction can often
be improved with domain knowledge. Target must be identical (or a very
good proxy) of the quantitative metric identified step 1.

119. Deriving Knowledge from Data at Scale
Feature selection
Model training
Model scoring
Evaluation
Train/ Test split
5. Train, test and evaluate, taking care to control
bias/variance and ensure the metrics are
reported with the right confidence intervals
(cross-validation helps here), be vigilant
against target leaks (which typically leads to
unbelievably good test metrics) – this is the
ML heavy step.

120. Deriving Knowledge from Data at Scale
Define
Objective
Access and
Understand
the data
Pre-processing
Feature and/or
Target
construction
Feature selection
Model training
Model scoring
Evaluation
Train/ Test split
6. Iterate steps (2) – (5) until the test metrics are satisfactory

121. Deriving Knowledge from Data at Scale
Access Data
Pre-processing
Feature
construction
Model scoring

122. Deriving Knowledge from Data at Scale

123. Deriving Knowledge from Data at Scale

124. Deriving Knowledge from Data at Scale
Book
Recommendation

125. Deriving Knowledge from Data at Scale
That’s all for our course….