With R, Python, Apache Spark and a plethora of other open source tools, anyone with a computer can run machine learning algorithms in a jiffy! However, without an understanding of which algorithms to choose and when to apply a particular technique, most machine learning efforts turn into trial and error experiments with conclusions like "The algorithms don't work" or "Perhaps we should get more data". In this lecture, we will focus on the key tenets of machine learning algorithms and how to choose an algorithm for a particular purpose. Rather than just showing how to run experiments in R ,Python or Apache Spark, we will provide an intuitive introduction to machine learning with just enough mathematics and basic statistics. We will address: • How do you differentiate Clustering, Classification and Prediction algorithms? • What are the key steps in running a machine learning algorithm? • How do you choose an algorithm for a specific goal? • Where does exploratory data analysis and feature engineering fit into the picture? • Once you run an algorithm, how do you evaluate the performance of an algorithm?

1. Location:
QuantUniversity Meetup
August 24th 2016
Boston MA
Machine Learning: An intuitive foundation
2016 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
www.QuantUniversity.com
sri@quantuniversity.com

2. 2
Slides and Code will be available at:
http://www.analyticscertificate.com

3. - Analytics Advisory services
- Custom training programs
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4. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
4

5. 5
Quantitative Analytics and Big Data Analytics Onboarding
• Trained more than 500 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Launching the Analytics Certificate
Program in September

7. 7
Quantitative Analytics and Big Data Analytics Onboarding
• Apply at:
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• Program starting September 18th
• Module 1:
▫ Sep 18th , 25th , Oct 2nd, 9th
• Module 2:
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• Module 3:
▫ Nov 13th, 20th, Dec 4th, Dec 11th
• Capstone + Certification Ceremony
▫ Dec 18th

8. 8
• September
▫ 11th, 12th : Spark Workshop, Boston
 www.analyticscertificate.com/SparkWorkshop
 Sponsored by IBM
▫ 19th, 20th : Anomaly Detection Workshop, New York
 www.analyticscertificate.com/AnomalyNYC
 Sponsored by Microsoft
Events of Interest

9. 9

10. Agenda
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation

11. 11

12. Dataset, variable and Observations
Dataset: A rectangular array with Rows as observations and
columns as variables
Variable: A characteristic of members of a population ( Age, State
etc.)
Observation: List of Variable values for a member of the
population

13. Variables
 A variable could be:
Categorical
 Yes/No flags
 AAA,BB ratings for bonds
Numerical
 35 mpg
 $170K salary

14. Datasets
• Longitudinal
▫ Observations are dependent
▫ Temporal-continuity is required
• Cross-sectional
▫ Observations are independent

15. 15
Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Summary

16. 16

17. 17
• Descriptive Statistics
▫ Goal is to describe the data at hand
▫ Backward looking
▫ Statistical techniques employed here
• Predictive Analytics
▫ Goal is to use historical data to build a model for prediction
▫ Forward looking
▫ Machine learning techniques employed here
Goal

18. 18
• How do you summarize numerical variables ?
• How do you summarize categorical variables ?
• How do you describe variability in numerical variables ?
• How do you summarize relationships between categorical and
numerical variables ?
• How do you summarize relationships between 2 numerical
variables?
Descriptive Statistics – Cross sectional datasets
See Data Analysis Taxonomy.xlsx

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• Goal is to extract the various components
Longitudinal datasets

20. 20
• Given a dataset, build a model that captures the similarities in
different observations and assigns them to different buckets.
• Given a set of variables, predict the value of another variable in a
given data set
▫ Predict Salaries given work experience, education etc.
▫ Predict whether a loan would be approved given fico score, current
loans, employment status etc.
Predictive Analytics : Cross sectional datasets

21. 21
• Given a time series dataset, build a model that can be used to
forecast values in the future
Predictive Analytics : Time series datasets

22. 22
Goal
Descriptive
Statistics
Cross
sectional
Numerical Categorical
Numerical vs
Categorical
Categorical vs
Categorical
Numerical vs
Numerical
Time series
Predictive
Analytics
Cross-
sectional
Segmentation Prediction
Predict a
number
Predict a
category
Time-series
Summary

23. 23

24. 24
Machine Learning Algorithms
Goal
Descriptive
Statistics
Cross
sectional
Numerical Categorical
Numerical vs
Categorical
Categorical vs
Categorical
Numerical vs
Numerical
Time series
Predictive
Analytics
Cross-
sectional
Segmentation Prediction
Predict a
number
Predict a
category
Time-series

25. 25
• Supervised Algorithms
▫ Given a set of variables 𝑥𝑖, predict the value of another variable 𝑦 in a
given data set such that
▫ If y is numeric => Prediction
▫ If y is categorical => Classification
Machine Learning
x1,x2,x3… Model F(X) y

26. 26
• Unsupervised Algorithms
▫ Given a dataset with variables 𝑥𝑖, build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
Machine Learning
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

27. 27
Supervised
Learning
algorithms
Parametric
models
Non-
Parametric
models
Supervised learning Algorithms - Prediction

28. 28
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
𝑌 = 𝛽0 + 𝛽1 𝑋1
Linear Regression Model Neural network Model

29. 29
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
K-nearest neighbor Model Decision tree Model

30. • Given estimates መ𝛽0, መ𝛽1, … , መ𝛽 𝑝We can make predictions using the
formula
ො𝑦 = መ𝛽0 + መ𝛽1 𝑥1 + መ𝛽2 𝑥2 + ⋯ + መ𝛽 𝑝 𝑥 𝑝
• The parameters are estimated using the least squares approach to
minimize the sum of squared errors
𝑅𝑆𝑆 = ෍
𝑖=1
𝑛
(𝑦𝑖 − ො𝑦𝑖)2
Multiple linear regression
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31. 31
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Logistic Regression, Neural Networks
Supervised Learning models - Classification
Logistic Regression Model Neural network Model

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• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
K-nearest neighbor Model Decision tree Model

33. 33
• Unsupervised Algorithms
▫ Given a dataset with variables 𝑥𝑖, build a model that captures the
similarities in different observations and assigns them to different
buckets => Clustering
Machine Learning
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

34. K-means clustering
• These methods partition the data into k clusters by assigning each data point to
its closest cluster centroid by minimizing the within-cluster sum of squares
(WSS), which is:
෍
𝑘=1
𝐾
෍
𝑖∈𝑆 𝑘
෍
𝑗=1
𝑃
(𝑥𝑖𝑗 − 𝜇 𝑘𝑗)2
where 𝑆 𝑘 is the set of observations in the kth cluster and 𝜇 𝑘𝑗 is the mean of jth
variable of the cluster center of the kth cluster.
• Then, they select the top n points that are the farthest away from their nearest
cluster centers as outliers.
34

35. 35
Anomaly Detection vs Unsupervised Learning

36. 36
Distance functions
• Euclidean distance:

37. 37
Distance functions
• Manhattan distance:
D =|𝑋𝐴- 𝑋 𝐵|+ |𝑌𝐴- 𝑌𝐵|

38. 38
Distance functions
• Correlation distance:

39. 39
Machine Learning Algorithms
Machine
Learning
Supervised
Prediction
Parametric
Linear
Regression
Neural
Networks
Non-
parametric
KNN Decision Trees
Classification
Parametric
Logistic
Regression
Neural
Networks
Non Parametric
Decision Trees KNN
Unsupervised
algorithms
K-means
Associative
rule mining

40. 40

41. 41
The Process
Data
cleansing
Feature
Engineering
Training
and Testing
Model
building
Model
selection

42. 42
• What transformations do I need for the x and y variables ?
• Which are the best features to use?
▫ Dimension Reduction – PCA
▫ Best subset selection
 Forward selection
 Backward elimination
 Stepwise regression
Feature Engineering

43. 43
Data
Training
80%
Testing
20%
Training the model

44. 44

45. 45
Evaluating
Machine
learning
algorithms
Supervised -
Prediction
R-square RMS MAE MAPE
Supervised-
Classification
Confusion
Matrix
ROC Curves
Evaluation framework

46. 46
• The prediction error for record i is defined as the difference
between its actual y value and its predicted y value
𝑒𝑖 = 𝑦𝑖 − ො𝑦𝑖
• 𝑅2 indicates how well data fits the statistical model
𝑅2 = 1 −
σ𝑖=1
𝑛
(𝑦𝑖 − ො𝑦𝑖)2
σ𝑖=1
𝑛
(𝑦𝑖 − ത𝑦𝑖)2
Prediction Accuracy Measures

47. 47
• Fit measures in classical regression modeling:
• Adjusted 𝑅2 has been adjusted for the number of predictors. It increases only
when the improve of model is more than one would expect to see by chance
(p is the total number of explanatory variables)
𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅2 = 1 −
Τσ𝑖=1
𝑛
(𝑦𝑖 − ො𝑦𝑖)2 (𝑛 − 𝑝 − 1)
σ𝑖=1
𝑛
𝑦𝑖 − ത𝑦𝑖
2 /(𝑛 − 1)
• MAE or MAD (mean absolute error/deviation) gives the magnitude of the
average absolute error
𝑀𝐴𝐸 =
1
𝑛 σ𝑖=1
𝑛
𝑒𝑖
Prediction Accuracy Measures

48. 48
▫ MAPE (mean absolute percentage error) gives a percentage score of
how predictions deviate on average
𝑀𝐴𝑃𝐸 =
1
𝑛 σ𝑖=1
𝑛
𝑒𝑖/𝑦𝑖
× 100%
• RMSE (root-mean-squared error) is computed on the training and
validation data
𝑅𝑀𝑆𝐸 = 1/𝑛 ෍
𝑖=1
𝑛
𝑒𝑖
2
Prediction Accuracy Measures

49. 49
• Consider a two-class case with classes 𝐶0 and 𝐶1
• Classification matrix:
Classification matrix
Predicted Class
Actual Class 𝐶0 𝐶1
𝐶0
𝑛0,0= number of 𝐶0 cases
classified correctly
𝑛0,1= number of 𝐶0 cases
classified incorrectly as 𝐶1
𝐶1
𝑛1,0= number of 𝐶1 cases
classified incorrectly as 𝐶0
𝑛1,1= number of 𝐶1 cases
classified correctly

50. 50
• Estimated misclassification rate (overall error rate) is a main
accuracy measure
𝑒𝑟𝑟 =
𝑛0,1 + 𝑛1,0
𝑛0,0 + 𝑛0,1 + 𝑛1,0 + 𝑛1,1
=
𝑛0,1 + 𝑛1,0
𝑛
• Overall accuracy:
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 1 − 𝑒𝑟𝑟 =
𝑛0,0 + 𝑛1,1
𝑛
Accuracy Measures

51. 51
• The ROC curve plots the pairs {sensitivity, 1-specificity}
as the cutoff value increases from 0 and 1
• Sensitivity (also called the true positive rate, or
the recall in some fields) measures the proportion of
positives that are correctly identified (e.g., the
percentage of sick people who are correctly identified
as having the condition).
• Specificity (also called the true negative rate) measures
the proportion of negatives that are correctly identified
as such (e.g., the percentage of healthy people who are
correctly identified as not having the condition).
• Better performance is reflected by curves that are
closer to the top left corner
ROC Curve

52. Agenda
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation

53. 53
Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Handling Data

54. 54
Goal
Descriptive
Statistics
Cross
sectional
Numerical Categorical
Numerical vs
Categorical
Categorical vs
Categorical
Numerical vs
Numerical
Time series
Predictive
Analytics
Cross-
sectional
Segmentation Prediction
Predict a
number
Predict a
category
Time-series
Goal

55. 55
Machine Learning Algorithms
Machine
Learning
Supervised
Prediction
Parametric
Linear
Regression
Neural
Networks
Non-
parametric
KNN Decision Trees
Classification
Parametric
Logistic
Regression
Neural
Networks
Non Parametric
Decision Trees KNN
Unsupervised
algorithms
K-means
Associative
rule mining

56. 56
The Process
Data
cleansing
Feature
Engineering
Training
and Testing
Model
building
Model
selection

57. 57
Evaluating
Machine
learning
algorithms
Supervised -
Prediction
R-square RMS MAE MAPE
Supervised-
Classification
Confusion
Matrix
ROC Curves
Evaluation framework

60. 60
www.analyticscertificate.com/SparkWorkshop

61. 61
Q&A
Slides, code and details about the Apache Spark Workshop
at: http://www.analyticscertificate.com/SparkWorkshop/

62. Thank you!
Members & Sponsors!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
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