Machine learning meetup

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 - Architecture assessments, advice and audits

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

6. 7 Quantitative Analytics and Big Data Analytics Onboarding • Apply at: www.analyticscertificate.com • Program starting September 18th • Module 1: ▫ Sep 18th , 25th , Oct 2nd, 9th • Module 2: ▫ Oct 16th , 23th , 30th, Nov 6th • Module 3: ▫ Nov 13th, 20th, Dec 4th, Dec 11th • Capstone + Certification Ceremony ▫ Dec 18th

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

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11. 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

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

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

14. 15 Data Cross sectional Numerical Categorical Longitudinal Numerical Summary

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16. 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

17. 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

18. 19 • Goal is to extract the various components Longitudinal datasets

19. 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

20. 21 • Given a time series dataset, build a model that can be used to forecast values in the future Predictive Analytics : Time series datasets

21. 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

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23. 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

24. 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

25. 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

26. 27 Supervised Learning algorithms Parametric models Non- Parametric models Supervised learning Algorithms - Prediction

27. 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

28. 29 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models K-nearest neighbor Model Decision tree Model

29. • 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 30

30. 31 • Parametric models ▫ Assume some functional form ▫ Fit coefficients • Examples : Logistic Regression, Neural Networks Supervised Learning models - Classification Logistic Regression Model Neural network Model

31. 32 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models K-nearest neighbor Model Decision tree Model

32. 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

33. 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

34. 35 Anomaly Detection vs Unsupervised Learning

35. 36 Distance functions • Euclidean distance:

36. 37 Distance functions • Manhattan distance: D =|𝑋𝐴- 𝑋 𝐵|+ |𝑌𝐴- 𝑌𝐵|

37. 38 Distance functions • Correlation distance:

38. 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

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40. 41 The Process Data cleansing Feature Engineering Training and Testing Model building Model selection

41. 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

42. 43 Data Training 80% Testing 20% Training the model

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44. 45 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

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

52. 53 Data Cross sectional Numerical Categorical Longitudinal Numerical Handling Data

53. 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

54. 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

55. 56 The Process Data cleansing Feature Engineering Training and Testing Model building Model selection

56. 57 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework

57. 60 www.analyticscertificate.com/SparkWorkshop

58. 61 Q&A Slides, code and details about the Apache Spark Workshop at: http://www.analyticscertificate.com/SparkWorkshop/

59. Thank you! Members & Sponsors! Sri Krishnamurthy, CFA, CAP Founder and CEO QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.com Contact Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be distributed or used in any other publication without the prior written consent of QuantUniversity LLC. 62